Skip to main content
SpringerLink
Log in

Eliminating Contextual Bias in Aspect-Based Sentiment Analysis

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14608))

Included in the following conference series:

  • 285 Accesses

Abstract

Pretrained language models (LMs) have made remarkable achievements in aspect-based sentiment analysis (ABSA). However, it is discovered that these models may struggle in some particular cases (e.g., to detect sentiments expressed towards targeted aspects with only implicit or adversarial expressions). Since it is hard for models to align implicit or adversarial expressions with their corresponding aspects, the sentiments of the targeted aspects would largely be impacted by the expressions towards other aspects in the sentence. We name this phenomenon as contextual bias. To tackle the problem, we propose a flexible aspect-oriented debiasing method (Arde) to eliminate the harmful contextual bias without the need of adjusting the underlying LMs. Intuitively, Arde calibrates the prediction towards the targeted aspect by subtracting the bias towards the context. Favorably, Arde can get theoretical support from counterfactual reasoning theory. Experiments are conducted on SemEval benchmark, and the results show that Arde can empirically improve the accuracy on contextually biased aspect sentiments without degrading the accuracy on unbiased ones. Driven by recent success of large language models (LLMs, e.g., ChatGPT), we further uncover that even LLMs can fail to address certain contextual bias, which yet can be effectively tackled by Arde.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cao, J., Liu, R., Peng, H., Jiang, L., Bai, X.: Aspect is not you need: no-aspect differential sentiment framework for aspect-based sentiment analysis. In: Carpuat, M., de Marneffe, M., Ruíz, I.V.M. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, 10–15 July 2022, pp. 1599–1609. Association for Computational Linguistics (2022). https://doi.org/10.18653/v1/2022.naacl-main.115

  2. 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)

    Google Scholar 

  3. Chen, S., Wang, Y., Liu, J., Wang, Y.: Bidirectional machine reading comprehension for aspect sentiment triplet extraction. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2–9 February 2021, pp. 12666–12674. AAAI Press (2021). https://ojs.aaai.org/index.php/AAAI/article/view/17500

  4. Chen, X., Sun, C., Wang, J., Li, S., Si, L., Zhang, M., Zhou, G.: Aspect sentiment classification with document-level sentiment preference modeling. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3667–3677 (2020)

    Google Scholar 

  5. Dai, J., Yan, H., Sun, T., Liu, P., Qiu, X.: Does syntax matter? a strong baseline for aspect-based sentiment analysis with roberta. In: Toutanova, K., et al. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, 6–11 June 2021, pp. 1816–1829. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.naacl-main.146

  6. Deng, P., Yuan, J., Zhao, Y., Qin, B.: Zero-shot aspect-level sentiment classification via explicit utilization of aspect-to-document sentiment composition. CoRR abs/2209.02276 (2022). https://doi.org/10.48550/arXiv.2209.02276

  7. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423

  8. Feng, F., Zhang, J., He, X., Zhang, H., Chua, T.: Empowering language understanding with counterfactual reasoning. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, 1–6 August 2021. Findings of ACL, vol. ACL/IJCNLP 2021, pp. 2226–2236. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.findings-acl.196

  9. Gao, L., Wang, Y., Liu, T., Wang, J., Zhang, L., Liao, J.: Question-driven span labeling model for aspect-opinion pair extraction. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2–9 February 2021, pp. 12875–12883. AAAI Press (2021). https://ojs.aaai.org/index.php/AAAI/article/view/17523

  10. He, M., Chen, X., Hu, X., Li, C.: Causal intervention for sentiment de-biasing in recommendation. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 4014–4018 (2022)

    Google Scholar 

  11. Hou, X., et al.: Graph ensemble learning over multiple dependency trees for aspect-level sentiment classification. In: Toutanova, K., et al. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, 6–11 June 2021, pp. 2884–2894. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.naacl-main.229

  12. Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) SBP-BRiMS 2018. LNCS, vol. 10899, pp. 197–206. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93372-6_22

    Chapter  Google Scholar 

  13. Kaushik, D., Hovy, E.H., Lipton, Z.C.: Learning the difference that makes a difference with counterfactually-augmented data. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020. OpenReview.net (2020). https://openreview.net/forum?id=Sklgs0NFvr

  14. Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, vol. 1: Long Papers, pp. 946–956. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/P18-1087. https://aclanthology.org/P18-1087/

  15. Li, Z., Zou, Y., Zhang, C., Zhang, Q., Wei, Z.: Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event/Punta Cana, Dominican Republic, 7–11 November 2021, pp. 246–256. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.emnlp-main.22

  16. Liu, R., Cao, J., Sun, N., Jiang, L.: Aspect feature distillation and enhancement network for aspect-based sentiment analysis. In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G. (eds.) SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11–15 July 2022, pp. 1577–1587. ACM (2022). https://doi.org/10.1145/3477495.3531938

  17. Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019). http://arxiv.org/abs/1907.11692

  18. Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 4068–4074. ijcai.org (2017). https://doi.org/10.24963/ijcai.2017/568

  19. Ma, F., Zhang, C., Song, D.: Exploiting position bias for robust aspect sentiment classification. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, 1–6 August 2021. Findings of ACL, vol. ACL/IJCNLP 2021, pp. 1352–1358. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.findings-acl.116

  20. Ma, F., Zhang, C., Zhang, B., Song, D.: Aspect-specific context modeling for aspect-based sentiment analysis. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds.) Natural Language Processing and Chinese Computing - 11th CCF International Conference, NLPCC 2022, Guilin, China, 24–25 September 2022, Proceedings, Part I. Lecture Notes in Computer Science, vol. 13551, pp. 513–526. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-17120-8_40

  21. Mao, Y., Shen, Y., Yu, C., Cai, L.: A joint training dual-mrc framework for aspect based sentiment analysis. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2–9 February 2021, pp. 13543–13551. AAAI Press (2021). https://ojs.aaai.org/index.php/AAAI/article/view/17597

  22. Pearl, J.: Causal inference in statistics: an overview (2009)

    Google Scholar 

  23. Peng, H., Xu, L., Bing, L., Huang, F., Lu, W., Si, L.: Knowing what, how and why: a near complete solution for aspect-based sentiment analysis. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7–12 February 2020, pp. 8600–8607. AAAI Press (2020). https://ojs.aaai.org/index.php/AAAI/article/view/6383

  24. Peper, J., Wang, L.: Generative aspect-based sentiment analysis with contrastive learning and expressive structure. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, 7–11 December 2022, pp. 6089–6095. Association for Computational Linguistics (2022). https://aclanthology.org/2022.findings-emnlp.451

  25. Phan, M.H., Ogunbona, P.O.: Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211–3220 (2020)

    Google Scholar 

  26. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35. Association for Computational Linguistics, Dublin (2014). https://doi.org/10.3115/v1/S14-2004. https://aclanthology.org/S14-2004

  27. Qian, C., Feng, F., Wen, L., Ma, C., Xie, P.: Counterfactual inference for text classification debiasing. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 5434–5445 (2021)

    Google Scholar 

  28. Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., Yang, D.: Is chatgpt a general-purpose natural language processing task solver? CoRR abs/2302.06476 (2023). https://doi.org/10.48550/arXiv.2302.06476

  29. Rietzler, A., Stabinger, S., Opitz, P., Engl, S.: Adapt or get left behind: Domain adaptation through BERT language model finetuning for aspect-target sentiment classification. In: Calzolari, N., et al. (eds.) Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, 11–16 May 2020, pp. 4933–4941. European Language Resources Association (2020). https://aclanthology.org/2020.lrec-1.607/

  30. Song, Y., Wang, J., Jiang, T., Liu, Z., Rao, Y.: Attentional encoder network for targeted sentiment classification. CoRR abs/1902.09314 (2019). http://arxiv.org/abs/1902.09314

  31. Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, vol. 1 (Long and Short Papers), pp. 380–385. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1035

  32. Sun, T., Wang, W., Jing, L., Cui, Y., Song, X., Nie, L.: Counterfactual reasoning for out-of-distribution multimodal sentiment analysis. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 15–23 (2022)

    Google Scholar 

  33. Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Su, J., Carreras, X., Duh, K. (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 214–224. The Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/d16-1021

  34. Tang, H., Ji, D., Li, C., Zhou, Q.: Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6578–6588 (2020)

    Google Scholar 

  35. Wang, B., Shen, T., Long, G., Zhou, T., Chang, Y.: Eliminating sentiment bias for aspect-level sentiment classification with unsupervised opinion extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3002–3012 (2021)

    Google Scholar 

  36. Wang, S., Mazumder, S., Liu, B., Zhou, M., Chang, Y.: Target-sensitive memory networks for aspect sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers (2018)

    Google Scholar 

  37. Wang, S., Zhou, J., Sun, C., Ye, J., Gui, T., Zhang, Q., Huang, X.: Causal intervention improves implicit sentiment analysis. In: Calzolari, N., et al. (eds.) Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, 12–17 October 2022, pp. 6966–6977. International Committee on Computational Linguistics (2022). https://aclanthology.org/2022.coling-1.607

  38. Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., He, X.: Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1791–1800 (2021)

    Google Scholar 

  39. Wu, Z., Ying, C., Zhao, F., Fan, Z., Dai, X., Xia, R.: Grid tagging scheme for aspect-oriented fine-grained opinion extraction. CoRR abs/2010.04640 (2020). https://arxiv.org/abs/2010.04640

  40. Wu, Z., Chen, Y., Kao, B., Liu, Q.: Perturbed masking: parameter-free probing for analyzing and interpreting BERT. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5–10 July 2020, pp. 4166–4176. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.383

  41. Xing, X., Jin, Z., Jin, D., Wang, B., Zhang, Q., Huang, X.: Tasty burgers, soggy fries: probing aspect robustness in aspect-based sentiment analysis. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, 16–20 November 2020, pp. 3594–3605. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.emnlp-main.292

  42. Xu, H., Liu, B., Shu, L., Yu, P.S.: BERT post-training for review reading comprehension and aspect-based sentiment analysis. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, vol. 1 (Long and Short Papers). pp, 2324–2335. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1242

  43. Xu, M., Wang, D., Feng, S., Yang, Z., Zhang, Y.: KC-ISA: an implicit sentiment analysis model combining knowledge enhancement and context features. In: Calzolari, N., et al. (eds.) Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, 12–17 October 2022, pp. 6906–6915. International Committee on Computational Linguistics (2022). https://aclanthology.org/2022.coling-1.601

  44. Xu, W., Li, X., Deng, Y., Bing, L., Lam, W.: Peerda: data augmentation via modeling peer relation for span identification tasks. CoRR abs/2210.08855 (2022). https://doi.org/10.48550/arXiv.2210.08855

  45. Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, vol. 1: Long Papers, pp. 2514–2523. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/P18-1234. https://aclanthology.org/P18-1234/

  46. Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 4567–4577. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/D19-1464

  47. Zhang, C., Li, Q., Song, D.: Syntax-aware aspect-level sentiment classification with proximity-weighted convolution network. In: Piwowarski, B., Chevalier, M., Gaussier, É., Maarek, Y., Nie, J., Scholer, F. (eds.) Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 1145–1148. ACM (2019). https://doi.org/10.1145/3331184.3331351

  48. Zhang, C., Li, Q., Song, D., Wang, B.: A multi-task learning framework for opinion triplet extraction. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16–20 November 2020. Findings of ACL, vol. EMNLP 2020, pp. 819–828. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.72

  49. Zhang, C., Ren, L., Ma, F., Wang, J., Wu, W., Song, D.: Structural bias for aspect sentiment triplet extraction. In: Calzolari, N., et al. (eds.) Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, 12–17 October 2022, pp. 6736–6745. International Committee on Computational Linguistics (2022). https://aclanthology.org/2022.coling-1.585

Download references

Acknowledgements

This work is funded in part by the Natural Science Foundation of China (grant no: 62376027) and Beijing Municipal Natural Science Foundation (grant no: 4222036 and IS23061).

Author information

Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Ruize An, Chen Zhang & Dawei Song

  2. The Open University, Milton Keynes, UK

    Dawei Song

Authors
  1. Ruize An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dawei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dawei Song .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

An, R., Zhang, C., Song, D. (2024). Eliminating Contextual Bias in Aspect-Based Sentiment Analysis. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14608. Springer, Cham. https://doi.org/10.1007/978-3-031-56027-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56027-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56026-2

  • Online ISBN: 978-3-031-56027-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation