Skip to main content

ABSA Methodology Based on Interval-Enhanced Talking-Heads Attention Network

  • Conference paper
  • First Online:
Artificial Neural Networks and Machine Learning – ICANN 2024 (ICANN 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15022))

Included in the following conference series:

  • 397 Accesses

Abstract

As an important task of fine-grained sentiment analysis, aspect-based sentiment classification faces many challenges. In order to improve the classification accuracy, this paper focuses on the problems of long-distance semantic feature capture, emotional noise filtering, opinion word and aspect matching. This paper proposes an Interval-enhanced Talking-heads Attention Network (ITAN) for aspect-based sentiment analysis. Firstly, the limited semantic interval enhancement module was introduced to limit the semantic interval according to the aspect relative distance threshold, and a variety of word combination relations were generated in the whole sentence. Then, the gated filtering operation is used to integrate and extract the information enhancement representation of the limited interval. At the same time, the talking-heads attention mechanism module is combined to capture the emotional information from different perspectives to ensure that the opinion words and aspects are effectively matched. Finally, the sentiment classifier is used to integrate the information to generate the sentiment representation. The proposed model achieves 86.47%, 82.59%, and 77.10% sentiment classification accuracy on three public datasets: Restaurant, Laptop, and Twitter, respectively. The experimental results show that the proposed model is effective and can effectively improve the performance of aspect-based sentiment classification tasks.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yan,K.: Aspect-level sentiment analysis method based on Transformer. Chongqing Technology and Business University (2023)

    Google Scholar 

  2. Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)

    Article  Google Scholar 

  3. Nazir, A., Rao, Y., Wu, L.: Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans. Affect. Comput. 13, 845–863 (2020)

    Article  Google Scholar 

  4. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2016)

    Google Scholar 

  5. Yu, T.R., Jin, R., Han, X.Z.: Review of pre-training models for natural language processing. Comput. Eng. Appl. 56, 12–22 (2020)

    Google Scholar 

  6. Li, Z.Y., Zou, Y.C., Zhang, C.: Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 246–256 (2021)

    Google Scholar 

  7. Xu, G.X., Zhang, Z.X., Zhang, T.: Aspect-level sentiment classification based on attention-BiLSTM model and transfer learning. Knowl.-Based Syst. 245, 108586 (2022)

    Article  Google Scholar 

  8. Chen, L.C., Lee, C.M., Chen, M.Y.: Exploration of social media for sentiment analysis using deep learning. Soft Comput. 24, 8187–8197 (2020)

    Article  Google Scholar 

  9. Chen, J.J.: Research on Target-level Sentiment Analysis of Texts Based on Deep Learning. National University of Defense Technology (2018)

    Google Scholar 

  10. Ma, N.: Research on Comment Text Sentiment Analysis and Interest Recommendation under Cross Domain. Liaoning Technical University (2023)

    Google Scholar 

  11. Tang, D.Y., Qin, B., Feng, X.C.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2015)

    Google Scholar 

  12. Thien, H.N., Kiyoaki, S.: Phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2509–2514 (2015)

    Google Scholar 

  13. Tang, D. Y., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network, In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 214–224 (2016)

    Google Scholar 

  14. Bao, L., Lambert, P., Badia, T.: Attention and lexicon regularized LSTM for aspect-based sentiment analysis. In: proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pp. 253–259. ACL, Florence (2019)

    Google Scholar 

  15. Ma, D.H., Li, S.J., Zhang, X.D., Wang, H.F.: Interactive attention networks for aspect-level sentiment classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4068–4074 (2017)

    Google Scholar 

  16. Huang, B.X., OuYang, L., Kathleen, M.C.: Aspect level sentiment classification with attention-over-attention neural networks. In: Proceedings of Social, Cultural, and Behavioral Modeling: 11th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 197–206 (2018)

    Google Scholar 

  17. Gu, S.Q., Zhang, L.P., Hou, Y.X., Song, Y.: A position-aware bidirectional attention network for aspect-level sentiment analysis. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 774–784 (2018)

    Google Scholar 

  18. Chen, P., Sun, Z.Q., Li, D.B.: 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 

  19. Zhang, C., Li, Q. C., Song, D. W.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 4568–4578 (2019)

    Google Scholar 

  20. Tian, Y.H., Chen, G.M., Song, Y.: Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2910–2922 (2021)

    Google Scholar 

  21. Li, R.F., Chen, H., Feng, F.X.: Dual graph convolutional networks for aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 6319–6329 (2021)

    Google Scholar 

  22. Li, X., Li, D.B., Wai, L., Bei, S.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018)

    Google Scholar 

  23. Zhao, C.Y.: Research on Aspect Level Sentiment Analysis Method that Merge Position Information and Opinion Span. Chongqing University of Posts and Telecommunications, (2022)

    Google Scholar 

  24. Liu, X.Y., Hou, R., Gan, Y.L., Luo, D., Shi, X.J., Liu, Q.: Aspect-oriented opinion alignment network for aspect-based sentiment classification. In: ECAI (2023)

    Google Scholar 

  25. He, Z.H., Chen, H.M.: Aspect based Sentiment Analysis is Based on Aspect Semantic and Gated Filtering Network. Southwest Jiaotong University (2023)

    Google Scholar 

  26. Shazeer, N.M., Lan, Z.Z., Cheng, Y.L.: Talking-Heads Attention. Arxiv (2020)

    Google Scholar 

  27. Lin, Z.C., Li, B.Z.: Aspect-based sentiment analysis based on local context focus mechanism and talking-head attention. Comput. Sci. (2022)

    Google Scholar 

  28. Maria, P., Dimitris, G., Haris, P.: Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30. Association for Computational Linguistics (2016)

    Google Scholar 

  29. Dong, L., Furu, W., Tan, C.Q.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yifan Huang .

Editor information

Editors and Affiliations

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

Wu, Y., Huang, Y., Yang, J., Zhao, Y., An, N., Feng, D. (2024). ABSA Methodology Based on Interval-Enhanced Talking-Heads Attention Network. In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15022. Springer, Cham. https://doi.org/10.1007/978-3-031-72350-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72350-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72349-0

  • Online ISBN: 978-3-031-72350-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics