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Advancing Aspect-Based Sentiment Analysis through Deep Learning Models (2404.03259v3)
Published 4 Apr 2024 in cs.CL and cs.AI
Abstract: Aspect-based sentiment analysis predicts sentiment polarity with fine granularity. While graph convolutional networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance. Specifically,we first integrate a bidirectional long short-term memory (Bi-LSTM) network and a self-attention-based transformer. This combination facilitates effective text encoding, preventing the loss of information and predicting long dependency text. A bidirectional GCN (Bi-GCN) with message passing is then employed to encode relationships between entities. Additionally, unnecessary information is filtered out using an aspect-specific masking technique. To validate the effectiveness of our proposed model, we conduct extensive evaluation experiments on four benchmark datasets. The experimental results demonstrate enhanced performance in aspect-based sentiment analysis with the use of SentiSys.
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ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, X., Li, C., Morimoto, Y.: A multi-factor approach for stock price prediction by using recurrent neural networks. Bulletin of networking, computing, systems, and software 8(1), 9–13 (2019) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017) Wang et al. [2016] Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016) Tang et al. [2015] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017) Wang et al. [2016] Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016) Tang et al. [2015] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016) Tang et al. [2015] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. 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[2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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[2015] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. 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In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016) Tang et al. [2015] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. 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In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. 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[2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. 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In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. ArXiv Preprint ArXiv:1512.01100 (2015) Chen et al. [2017] Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017) Ma et al. [2017] Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. ArXiv Preprint ArXiv:1709.00893 (2017) Fan et al. [2018] Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. 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[2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Fan, F., Feng, Y., Zhao, D.: Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442 (2018) Rani et al. [2022] Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. 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[2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. 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[2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. 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Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rani, S., Bashir, A.K., Alhudhaif, A., Koundal, D., Gunduz, E.S., et al.: An efficient CNN-LSTM model for sentiment detection in #blacklivesmatter. Expert Systems with Applications 193, 116256 (2022) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907 (2016) Poria et al. [2014] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. 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In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. 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International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. 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Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. 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In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, pp. 41–47 (2014) Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI open 1, 57–81 (2020) Meng et al. [2020] Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Meng, F., Feng, J., Yin, D., Chen, S., Hu, M.: A structure-enhanced graph convolutional network for sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 586–595 (2020) Chang et al. [2023] Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. 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[2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. 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In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chang, M., Yang, M., Jiang, Q., Xu, R.: Reducing spurious correlations for aspect-based sentiment analysis with variational information bottleneck and contrastive learning. ArXiv Preprint ArXiv:2303.02846 (2023) Scaria et al. [2023] Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Scaria, K., Gupta, H., Sawant, S.A., Mishra, S., Baral, C.: Instructabsa: Instruction learning for aspect based sentiment analysis. ArXiv Preprint ArXiv:2302.08624 (2023) Li et al. [2023] Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. 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International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. 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[2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Ju, P., Morimoto, Y.: Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks. Procedding of 10th International Conference on Dependable Systems and Their Applications (DSA), 722–729 (2023) Rao and Ravichandran [2009] Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. 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In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 675–682 (2009) Ding et al. [2008] Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008) Jiang et al. [2011] Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. 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In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. 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[2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. 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In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
- Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 151–160 (2011) Cai et al. [2020] Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843 (2020) Manek et al. [2017] Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Manek, A.S., Shenoy, P.D., Mohan, M.C.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and SVM classifier. World wide web 20, 135–154 (2017) Zhu et al. [2019] Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(6), 1–21 (2019) Ma et al. [2019] Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. 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[2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. 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In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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[2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. 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IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. 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[2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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[2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, D., Li, S., Wu, F., Xie, X., Wang, H.: Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3538–3547 (2019) Kumar et al. [2021] Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. 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In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Kumar, S., Ghosal, T., Bharti, P.K., Ekbal, A.: Sharing is caring! joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 270–273 (2021). IEEE Phan and Ogunbona [2020] Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. 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[2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. 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In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020) Liao et al. [2021] Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 51, 3522–3533 (2021) Augustyniak et al. [2021] Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. 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[2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Augustyniak, Ł., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. Computer Speech & Language 69, 101217 (2021) Do et al. [2019] Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. 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[2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. 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ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
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In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: A comparative review. Expert systems with applications 118, 272–299 (2019) Li et al. [2018] Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., He, M., Qaosar, M., Ahmed, S., Morimoto, Y.: Capturing temporal dynamics of users’ preferences from purchase history big data for recommendation system. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5372–5374 (2018) Li et al. [2022] Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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[2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. 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[2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
- Li, C., Yamanaka, C., Kaitoh, K., Yamanishi, Y.: Transformer-based objective-reinforced generative adversarial network to generate desired molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 3884–3890 (2022) Li and Yamanishi [2023] Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Yamanishi, Y.: Spotgan: A reverse-transformer gan generates scaffold-constrained molecules with property optimization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 323–338 (2023). Springer Li et al. [2022] Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. 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[2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, C., Zheng, J., Okamura, H., Dohi, T.: Software reliability prediction through encoder-decoder recurrent neural networks. International Journal of Mathematical, Engineering and Management Sciences 7(3), 325 (2022) Tang et al. [2023] Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, H., Li, C., Jiang, S., Yu, H., Kamei, S., Yamanishi, Y., Morimoto, Y.: Earlgan: An enhanced actor–critic reinforcement learning agent-driven gan for de novo drug design. Pattern Recognition Letters 175, 45–51 (2023) Tang et al. [2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. 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[2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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[2016] Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. 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[2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016) Li et al. [2018] Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018) Ma et al. [2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. 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In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. 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[2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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[2018] Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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[2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. 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In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Chen and Qian [2020] Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694 (2020) Wang et al. [2020] Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238 (2020) Zhang et al. [2019] Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. 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[2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. 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[2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4568–4578 (2019) Xiao et al. [2020] Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. 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In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. 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[2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
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[2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Xiao, L., Hu, X., Chen, Y., Xue, Y., Gu, D., Chen, B., Zhang, T.: Targeted sentiment classification based on attentional encoding and graph convolutional networks. Applied Sciences 10(3), 957 (2020) Zhou et al. [2020] Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. 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[2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
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[2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
- Zhou, J., Huang, J.X., Hu, Q.V., He, L.: SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems 205, 106292 (2020) Zhao et al. [2022] Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhao, M., Yang, J., Zhang, J., Wang, S.: Aggregated graph convolutional networks for aspect-based sentiment classification. Information Sciences 600, 73–93 (2022) Graves et al. [2013] Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. 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In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
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In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
- Graves, A., Mohamed, A.-r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013) Hu et al. [2021] Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Hu, Y., Shen, H., Liu, W., Min, F., Qiao, X., Jin, K.: A graph convolutional network with multiple dependency representations for relation extraction. IEEE Access 9, 81575–81587 (2021) Cortes et al. [2009] Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116 (2009) Dong et al. [2014] Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. 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[2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. 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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
- Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014) Pontiki et al. [2014] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014) Pontiki et al. [2015] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. 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In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
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In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015) Pontiki et al. [2016] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. [2017] Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017) Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al.: Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016) Pennington et al. [2014] Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Zhang [2018] Zhang, Z.: Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018) Tang et al. [2016] Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. ArXiv Preprint ArXiv:1605.08900 (2016) Huang et al. [2018] Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of the 11th International Conference on SBP-BRiMS 2018, pp. 197–206 (2018) Wang et al. 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