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A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends (2311.10777v6)

Published 16 Nov 2023 in cs.CL and cs.LG

Abstract: Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.

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References (110)
  1. Sentiment analysis as a restricted NLP problem:. In Fatih Pinarbasi and M. Nurdan Taskiran, editors, Advances in Business Information Systems and Analytics, pages 65–96. IGI Global, 2021.
  2. Intelligent learning based opinion mining model for governmental decision making. Procedia Computer Science, 173:216–224, 2020.
  3. A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7):5731–5780, 2022.
  4. Explicit aspects extraction in sentiment analysis using optimal rules combination. Future Generation Computer Systems, 114:448–480, 2021.
  5. W2vlda: Almost unsupervised system for aspect based sentiment analysis. Expert Systems with Applications, 91:127–137, 2018.
  6. Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 4465–4473, Vancouver, BC, Canada, 2016. IEEE.
  7. An unsupervised aspect extraction strategy for monitoring real-time reviews stream. Information Processing & Management, 56(3):1103–1118, 2019.
  8. Scalable aspect-based summarization in the hadoop environment. In V. B. Aggarwal, Vasudha Bhatnagar, and Durgesh Kumar Mishra, editors, Big Data Analytics, volume 654, pages 439–449. Springer Singapore, Singapore, 2018. Series Title: Advances in Intelligent Systems and Computing.
  9. Exploiting guest preferences with aspect-based sentiment analysis for hotel recommendation. In Ana Fred, Jan L.G. Dietz, David Aveiro, Kecheng Liu, and Joaquim Filipe, editors, Knowledge Discovery, Knowledge Engineering and Knowledge Management, volume 631, pages 31–46. Springer International Publishing, Cham, 2016. Series Title: Communications in Computer and Information Science.
  10. Application of text mining in smart lighting literature - an analysis of existing literature and a research agenda. International Journal of Information Management Data Insights, 1(2):100032, 2021.
  11. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowledge-Based Systems, 235:107643, 2022.
  12. Multi-domain aspect extraction based on deep and lifelong learning. In Ingela Nyström, Yanio Hernández Heredia, and Vladimir Milián Núñez, editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, volume 11896, pages 556–565. Springer International Publishing, Cham, 2019. Series Title: Lecture Notes in Computer Science.
  13. Bidirectional independently long short-term memory and conditional random field integrated model for aspect extraction in sentiment analysis. In Suresh Chandra Satapathy, Vikrant Bhateja, Bao Le Nguyen, Nhu Gia Nguyen, and Dac-Nhuong Le, editors, Frontiers in Intelligent Computing: Theory and Applications, volume 1014, pages 131–140. Springer Singapore, Singapore, 2020. Series Title: Advances in Intelligent Systems and Computing.
  14. Multi-task learning for aspect term extraction and aspect sentiment classification. Neurocomputing, 398:247–256, 2020.
  15. A survey on aspect-based sentiment classification. ACM Computing Surveys, 55(4):1–37, 2023.
  16. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177, Seattle WA USA, 2004. ACM.
  17. Mining opinion features in customer reviews. In Proceedings of the 19th National Conference on Artifical Intelligence, AAAI’04, pages 755–760, San Jose, California, 2004. AAAI Press.
  18. Aspect-based rating prediction on reviews using sentiment strength analysis. In Salem Benferhat, Karim Tabia, and Moonis Ali, editors, Advances in Artificial Intelligence: From Theory to Practice, volume 10351, pages 439–447. Springer International Publishing, Cham, 2017. Series Title: Lecture Notes in Computer Science.
  19. Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. IEEE Transactions on Affective Computing, 13(2):845–863, 2022.
  20. Aspect-based sentiment analysis: A survey of deep learning methods. IEEE Transactions on Computational Social Systems, 7(6):1358–1375, 2020.
  21. ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining. Knowledge-Based Systems, 253:109511, 2022.
  22. Evaluation of weakly-supervised methods for aspect extraction. Procedia Computer Science, 207:2688–2697, 2022.
  23. A systematic review on implicit and explicit aspect extraction in sentiment analysis. IEEE Access, 8:194166–194191, 2020.
  24. Over a decade of social opinion mining: a systematic review. Artificial Intelligence Review, 54(7):4873–4965, 2021.
  25. A knowledge-based approach for aspect-based opinion mining. In Harald Sack, Stefan Dietze, Anna Tordai, and Christoph Lange, editors, Semantic Web Challenges, volume 641, pages 141–152. Springer International Publishing, Cham, 2016. Series Title: Communications in Computer and Information Science.
  26. Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems with Applications, 118:272–299, 2019.
  27. Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 46:459–483, 2016.
  28. Beyond the stars: Improving rating predictions using review text content. In International Workshop on the Web and Databases, 2009.
  29. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, page 168–177, New York, NY, USA, 2004. Association for Computing Machinery.
  30. Minqing Hu and B. Liu. Mining opinion features in customer reviews. In AAAI Conference on Artificial Intelligence, 2004.
  31. SemEval-2014 task 4: Aspect based sentiment analysis. In Preslav Nakov and Torsten Zesch, editors, Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27–35, Dublin, Ireland, August 2014. Association for Computational Linguistics.
  32. SemEval-2015 task 12: Aspect based sentiment analysis. In Preslav Nakov, Torsten Zesch, Daniel Cer, and David Jurgens, editors, Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 486–495, Denver, Colorado, June 2015. Association for Computational Linguistics.
  33. SemEval-2016 task 5: Aspect based sentiment analysis. In Steven Bethard, Marine Carpuat, Daniel Cer, David Jurgens, Preslav Nakov, and Torsten Zesch, editors, Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 19–30, San Diego, California, June 2016. Association for Computational Linguistics.
  34. Aspect-based opinion mining in drug reviews. In Eugénio Oliveira, João Gama, Zita Vale, and Henrique Lopes Cardoso, editors, Progress in Artificial Intelligence, volume 10423, pages 815–827. Springer International Publishing, Cham, 2017. Series Title: Lecture Notes in Computer Science.
  35. Understanding patient reviews with minimum supervision. Artificial Intelligence in Medicine, 120:102160, 2021.
  36. Survey of aspect-based sentiment analysis datasets, 2023.
  37. Comparison, classification and survey of aspect based sentiment analysis. In Ashish Kumar Luhach, Dharm Singh, Pao-Ann Hsiung, Kamarul Bin Ghazali Hawari, Pawan Lingras, and Pradeep Kumar Singh, editors, Advanced Informatics for Computing Research, pages 612–629, Singapore, 2019. Springer Singapore.
  38. A survey on implicit aspect detection for sentiment analysis: Terminology, issues, and scope. IEEE Access, 10:63932–63957, 2022.
  39. Vaishali Ganganwar and R. Rajalakshmi. Implicit aspect extraction for sentiment analysis: A survey of recent approaches. Procedia Computer Science, 165:485–491, 2019. 2nd International Conference on Recent Trends in Advanced Computing ICRTAC -DISRUP - TIV INNOVATION , 2019 November 11-12, 2019.
  40. Deep learning for aspect-level sentiment classification: Survey, vision, and challenges. IEEE Access, 7:78454–78483, 2019.
  41. Deep learning for aspect-based sentiment analysis. In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), pages 267–271, 2021.
  42. Deep learning techniques for aspect based sentiment analysis. In 2022 14th International Conference on Computer Research and Development (ICCRD), pages 69–73, 2022.
  43. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY, USA, 2017. Curran Associates Inc.
  44. Christopher D. Manning. Human Language Understanding & Reasoning. Daedalus, 151(2):127–138, May 2022.
  45. Guidelines for performing systematic literature reviews in software engineering, 2007. Backup Publisher: Keele University and Durham University Joint Report.
  46. Systematic literature review of arabic aspect-based sentiment analysis. Journal of King Saud University - Computer and Information Sciences, 34(9):6524–6551, 2022.
  47. Arabic aspect-based sentiment analysis: A systematic literature review. IEEE Access, 9:152628–152645, 2021.
  48. Text mining, clustering and sentiment analysis: A systematic literature review. In 2022 11th Mediterranean Conference on Embedded Computing (MECO), pages 1–6, Budva, Montenegro, 2022. IEEE.
  49. A journey of indian languages over sentiment analysis: a systematic review. Artificial Intelligence Review, 52(2):1415–1462, 2019.
  50. Opinion mining for software development: A systematic literature review. ACM Transactions on Software Engineering and Methodology, 31(3):1–41, 2022.
  51. Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review, 54(7):4997–5053, 2021.
  52. IAF-LG: An interactive attention fusion network with local and global perspective for aspect-based sentiment analysis. IEEE Transactions on Affective Computing, 13(4):1730–1742, 2022.
  53. Non-negative matrix factorization for implicit aspect identification. Journal of Ambient Intelligence and Humanized Computing, 11(7):2683–2699, July 2020.
  54. Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowledge-Based Systems, 125:116–135, 2017.
  55. SK2: Integrating implicit sentiment knowledge and explicit syntax knowledge for aspect-based sentiment analysis. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 1114–1123, Atlanta GA USA, 2022. ACM.
  56. Aspect-based sentiment analysis of students’ feedback to improve teaching–learning process. In Suresh Chandra Satapathy and Amit Joshi, editors, Information and Communication Technology for Intelligent Systems, volume 107, pages 259–266. Springer Singapore, Singapore, 2019. Series Title: Smart Innovation, Systems and Technologies.
  57. Aspect based sentiment analysis: Category detection and sentiment classification for hindi. In Alexander Gelbukh, editor, Computational Linguistics and Intelligent Text Processing, volume 9624, pages 246–257. Springer International Publishing, Cham, 2018. Series Title: Lecture Notes in Computer Science.
  58. Weighted aspect-based opinion mining using deep learning for recommender system. Expert Systems with Applications, 140:112871, 2020.
  59. Aspect-based summarization for game review using double propagation. In 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), pages 1–6, Denpasar, 2017. IEEE.
  60. Sentic computing for aspect-based opinion summarization using multi-head attention with feature pooled pointer generator network. Cognitive Computation, 14(1):130–148, 2022.
  61. Improving MOOCs using information from discussion forums: An opinion summarization and suggestion mining approach. IEEE Access, 10:15565–15573, 2022.
  62. Finding users preferences from large-scale online reviews for personalized recommendation. Electronic Commerce Research, 17(1):3–29, 2017.
  63. Product’s behaviour recommendations using free text: an aspect based sentiment analysis approach. Cluster Computing, 23(2):1267–1279, 2020.
  64. A multi-task dual-encoder framework for aspect sentiment triplet extraction. IEEE Access, 10:103187–103199, 2022.
  65. Deep2s: Improving aspect extraction in opinion mining with deep semantic representation. IEEE Access, 8:104026–104038, 2020.
  66. Nonautoregressive encoder–decoder neural framework for end-to-end aspect-based sentiment triplet extraction. IEEE Transactions on Neural Networks and Learning Systems, 34(9):5544–5556, 2023.
  67. Detecting dependency-related sentiment features for aspect-level sentiment classification. IEEE Transactions on Affective Computing, 14(1):196–210, 2023.
  68. Improving aspect term extraction with bidirectional dependency tree representation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(7):1201–1212, 2019.
  69. Sequential covering rule learning for language rule-based aspect extraction. In 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS), pages 229–234, Bali, Indonesia, 2019. IEEE.
  70. Recurrent neural CRF for aspect term extraction with dependency transmission. In Min Zhang, Vincent Ng, Dongyan Zhao, Sujian Li, and Hongying Zan, editors, Natural Language Processing and Chinese Computing, volume 11108, pages 378–390. Springer International Publishing, Cham, 2018. Series Title: Lecture Notes in Computer Science.
  71. Omer Gunes. Aspect term and opinion target extraction from web product reviews using semi-markov conditional random fields with word embeddings as features. In Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, pages 1–5, Nîmes France, 2016. ACM.
  72. Memory networks for fine-grained opinion mining. Artificial Intelligence, 265:1–17, 2018.
  73. Aspect and opinion terms extraction using double embeddings and attention mechanism for indonesian hotel reviews. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA), pages 1–6, Yogyakarta, Indonesia, 2019. IEEE.
  74. Aspect based sentiment analysis for review rating prediction. In 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA), pages 1–6, Penang, Malaysia, August 2016. IEEE.
  75. Lci: A social channel analysis platform for live customer intelligence. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD ’11, page 1049–1058, New York, NY, USA, 2011. Association for Computing Machinery.
  76. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, and Daniel Tapias, editors, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, May 2010. European Language Resources Association (ELRA).
  77. SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives. In Yuji Matsumoto and Rashmi Prasad, editors, Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2666–2677, Osaka, Japan, December 2016. The COLING 2016 Organizing Committee.
  78. Cross-domain aspect detection and categorization using machine learning for aspect-based opinion mining. International Journal of Information Management Data Insights, 2(2):100099, 2022.
  79. A span-sharing joint extraction framework for harvesting aspect sentiment triplets. Knowledge-Based Systems, 242:108366, 2022.
  80. IAOTP: An interactive end-to-end solution for aspect-opinion term pairs extraction. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1588–1598, Madrid Spain, 2022. ACM.
  81. A multi-task learning framework for end-to-end aspect sentiment triplet extraction. Neurocomputing, 479:12–21, March 2022.
  82. A survey on aspect-based sentiment analysis: Tasks, methods, and challenges, 2022.
  83. Span-based relational graph transformer network for aspect–opinion pair extraction. Knowledge and Information Systems, 64(5):1305–1322, 2022.
  84. High-order pair-wise aspect and opinion terms extraction with edge-enhanced syntactic graph convolution. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:2396–2406, 2021.
  85. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 504–515, Florence, Italy, 2019. Association for Computational Linguistics.
  86. Syntax-type-aware graph convolutional networks for natural language understanding. Applied Soft Computing, 102:107080, 2021.
  87. Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 1319–1328, Shanghai China, 2014. ACM.
  88. Cross-domain aspect extraction using transformers augmented with knowledge graphs. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 780–790, Atlanta GA USA, 2022. ACM.
  89. An enhanced method for review mining using n-gram approaches. In Jennifer S. Raj, Abdullah M. Iliyasu, Robert Bestak, and Zubair A. Baig, editors, Innovative Data Communication Technologies and Application, volume 59, pages 615–626. Springer Singapore, Singapore, 2021. Series Title: Lecture Notes on Data Engineering and Communications Technologies.
  90. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.
  91. Language models are few-shot learners, 2020.
  92. Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3211–3220, Online, 2020. Association for Computational Linguistics.
  93. Retrieve-and-edit domain adaptation for end2end aspect based sentiment analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:659–672, 2022.
  94. On the robustness of aspect-based sentiment analysis: Rethinking model, data, and training. ACM Transactions on Information Systems, 41(2):1–32, 2023.
  95. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, page 3104–3112, Montreal, Canada, 2014. MIT Press.
  96. Tasty burgers, soggy fries: Probing aspect robustness in aspect-based sentiment analysis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3594–3605, Online, 2020. Association for Computational Linguistics.
  97. Wikipedia. SemEval, 2023.
  98. A Convolutional Stacked Bidirectional LSTM with a Multiplicative Attention Mechanism for Aspect Category and Sentiment Detection. Cognitive Computation, 13(6):1423–1432, November 2021.
  99. Convolutional multi-head self-attention on memory for aspect sentiment classification. IEEE/CAA Journal of Automatica Sinica, 7(4):1038–1044, July 2020.
  100. Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification. IEEE Access, 8:139346–139355, 2020.
  101. Interactive Double Graph Convolutional Networks for Aspect-based Sentiment Analysis. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–7, Padua, Italy, July 2022. IEEE.
  102. A challenge dataset and effective models for aspect-based sentiment analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6279–6284, Hong Kong, China, 2019. Association for Computational Linguistics.
  103. Multi-grained attention network for aspect-level sentiment classification. In Conference on Empirical Methods in Natural Language Processing, 2018.
  104. What do we think we think we are doing?: Metacognition and self-regulation in programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research, pages 2–13, Virtual Event New Zealand, 2020. ACM.
  105. Combining adversarial training and relational graph attention network for aspect-based sentiment analysis with bert. In 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pages 1–6, 2021.
  106. An rl approach for absa using transformers. In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pages 354–361, 2022.
  107. Aspect-based sentiment classification via reinforcement learning. In 2021 IEEE International Conference on Data Mining (ICDM), pages 1391–1396, 2021.
  108. OpenAI. Chatgpt (mar 14 version) [large language model]. https://chat.openai.com/chat, 2023.
  109. Johan S. G. Chu and James A. Evans. Slowed canonical progress in large fields of science. Proceedings of the National Academy of Sciences, 118(41):e2021636118, 2021.
  110. The Pandas Development Team. pandas-dev/pandas: Pandas, 2023.
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Authors (4)
  1. Yan Cathy Hua (1 paper)
  2. Paul Denny (67 papers)
  3. Katerina Taskova (1 paper)
  4. Jörg Wicker (6 papers)
Citations (2)
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