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BLP-2023 Task 2: Sentiment Analysis (2310.16183v2)

Published 24 Oct 2023 in cs.CL and cs.LG

Abstract: We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluation phases, respectively. In total, participants submitted 597 runs. However, a total of 15 teams submitted system description papers. The range of approaches in the submitted systems spans from classical machine learning models, fine-tuning pre-trained models, to leveraging LLM (LLMs) in zero- and few-shot settings. In this paper, we provide a detailed account of the task setup, including dataset development and evaluation setup. Additionally, we provide a brief overview of the systems submitted by the participants. All datasets and evaluation scripts from the shared task have been made publicly available for the research community, to foster further research in this domain.

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References (55)
  1. A review of bangla natural language processing tasks and the utility of transformer models. arXiv preprint arXiv:2107.03844.
  2. Humaid: Human-annotated disaster incidents data from twitter with deep learning benchmarks. In Proceedings of the International AAAI Conference on Web and social media, volume 15, pages 933–942.
  3. Data set for sentiment analysis on bengali news comments and its baseline evaluation. In Proc. of ICBSLP, pages 1–5. IEEE.
  4. A deep recurrent neural network with bilstm model for sentiment classification. In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pages 1–4. IEEE.
  5. Nayan Banik and Md Hasan Hafizur Rahman. 2018. Evaluation of naïve bayes and support vector machines on Bangla textual movie reviews. In Proc. of ICBSLP, pages 1–6. IEEE.
  6. Reliable baselines for sentiment analysis in resource-limited languages: The serbian movie review dataset. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 2688–2696.
  7. BanglaBERT: Language model pretraining and benchmarks for low-resource language understanding evaluation in Bangla. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1318–1327, Seattle, United States. Association for Computational Linguistics.
  8. Banglanlg: Benchmarks and resources for evaluating low-resource natural language generation in bangla. CoRR, abs/2205.11081.
  9. Aunabil Chakma and Masum Hasan. 2023. Lowresource at BLP-2023 Task 2: Leveraging banglabert for low resource sentiment analysis of bangla language. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  10. Analyzing sentiment of movie reviews in Bangla by applying machine learning techniques. In Proc. of (ICBSLP), pages 1–6. IEEE.
  11. Shaika Chowdhury and Wasifa Chowdhury. 2014. Performing sentiment analysis in Bangla microblog posts. In 2014 International Conference on Informatics, Electronics Vision (ICIEV), pages 1–6.
  12. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416.
  13. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL ’20, pages 8440–8451, Online. Association for Computational Linguistics.
  14. Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review, pages 1–42.
  15. Team error point at BLP-2023 Task 2: A comparative exploration of hybrid deep learning and machine learning approach for advanced sentiment analysis techniques. In Proceedings of the 1st Workshop on Bangla Language Processing (BLP 2023), Singapore. Association for Computational Linguistics.
  16. Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cognitive computation, 8:757–771.
  17. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT ’19, pages 4171–4186, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
  18. Semantics squad at BLP-2023 Task 2: Sentiment analysis of bengali text with fine tuned transformer based models. In Proceedings of the 1st Workshop on Bangla Language Processing (BLP 2023), Singapore. Association for Computational Linguistics.
  19. Md Fahim. 2023. Aambela at BLP-2023 Task 2: Enhancing banglabert performance for bangla sentiment analysis task with in task pretraining and adversarial weight perturbation. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  20. Emptymind at BLP-2023 Task 2: Sentiment analysis of bangla social media posts using transformer-based models. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  21. nlpbdpatriots at BLP-2023 Task 2: A transfer learning approach towards bangla sentiment analysis. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  22. Zero-and few-shot prompting with llms: A comparative study with fine-tuned models for bangla sentiment analysis. arXiv preprint arXiv:2308.10783.
  23. Sentiment classification in bangla textual content: a comparative study. In 2020 23rd International Conference on Computer and Information Technology (ICCIT), pages 1–6. IEEE.
  24. Sentiment analysis on Bangla and romanized Bangla text using deep recurrent models. In 2016 International Workshop on Computational Intelligence (IWCI), pages 51–56. IEEE.
  25. Doaa Mohey El-Din Mohamed Hussein. 2018. A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4):330–338.
  26. SentNoB: A dataset for analysing sentiment on noisy bangla texts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3265–3271.
  27. SentiGOLD: A large bangla gold standard multi-domain sentiment analysis dataset and its evaluation. arXiv preprint arXiv:2306.06147.
  28. Supervised approach of sentimentality extraction from Bengali facebook status. In 2016 19th International Conference on Computer and Information Technology (ICCIT), pages 383–387. IEEE.
  29. BanglaBook: A large-scale Bangla dataset for sentiment analysis from book reviews. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1237–1247, Toronto, Canada. Association for Computational Linguistics.
  30. Indicnlpsuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for indian languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4948–4961.
  31. Classification benchmarks for under-resourced Bengali language based on multichannel convolutional-lstm network. CoRR, abs / 2004.07807.
  32. Muril: Multilingual representations for indian languages. arXiv preprint arXiv:2103.10730.
  33. Ushoshi2023 at BLP-2023 Task 2: A comparison of traditional to advanced linguistic models to analyze sentiment in bangla texts. In Proceedings of the 1st Workshop on Bangla Language Processing (BLP 2023), Singapore. Association for Computational Linguistics.
  34. Bing Liu. 2020. Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press.
  35. Knowdee at BLP-2023 Task 2: Improving bangla sentiment analysis using ensembled models with pseudo-labeling. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  36. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  37. Crosslingual generalization through multitask finetuning. arXiv preprint arXiv:2211.01786.
  38. Afrisenti: A twitter sentiment analysis benchmark for african languages. arXiv preprint arXiv:2302.08956.
  39. Ufal-uld at blp-2023 task 2 sentiment classification in bangla text. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  40. Astd: Arabic sentiment tweets dataset. In Proceedings of the 2015 conference on empirical methods in natural language processing, pages 2515–2519.
  41. R OpenAI. 2023. Gpt-4 technical report. arXiv, pages 2303–08774.
  42. Shared task on sentiment analysis in indian languages (sail) tweets-an overview. In Mining Intelligence and Knowledge Exploration: Third International Conference, MIKE 2015, Hyderabad, India, December 9-11, 2015, Proceedings 3, pages 650–655. Springer.
  43. Sentiment analysis on movie review data using machine learning approach. In 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), pages 1–4.
  44. Majidur Rahman and Özlem Uzuner. 2023. M1437 at BLP-2023 Task 2: Harnessing bangla text for sentiment analysis: A transformer-based approach. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  45. SemEval-2017 task 4: Sentiment analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 502–518, Vancouver, Canada. Association for Computational Linguistics.
  46. Saumajit Saha and Albert Nanda. 2023. Banglanlp at BLP-2023 Task 2: Benchmarking different transformer models for sentiment analysis of bangla social media posts. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  47. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
  48. Salim Sazzed. 2021. Improving sentiment classification in low-resource bengali language utilizing cross-lingual self-supervised learning. In International Conference on Applications of Natural Language to Information Systems, pages 218–230. Springer.
  49. Rsm-nlp at BLP-2023 Task 2: Bangla sentiment analysis using weighted and majority voted fine-tuned transformers. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  50. Sadia Sharmin and Danial Chakma. 2021. Attention-based convolutional neural network for bangla sentiment analysis. Ai & Society, 36(1):381–396.
  51. Z-index at BLP-2023 Task 2: A comparative study on sentiment analysis. In Proceedings of the 1st Workshop on Bangla Language Processing (BLP 2023), Singapore. Association for Computational Linguistics.
  52. S.M Towhidul Islam Tonmoy. 2023. Embeddings at BLP-2023 Task 2: Optimizing fine-tuned transformers with cost-sensitive learning for multiclass sentiment analysis. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  53. Nafis Irtiza Tripto and Mohammed Eunus Ali. 2018. Detecting multilabel sentiment and emotions from Bangla youtube comments. In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pages 1–6. IEEE.
  54. Lowresourcenlu at blp: Enhancing sentiment classification and violence incitement detection through aggregated language models. In Proceedings of the 1st International Workshop on Bangla Language Processing (BLP-2023), Singapore. Association for Computational Linguistics.
  55. mt5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 483–498.
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