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.
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References
Yan,K.: Aspect-level sentiment analysis method based on Transformer. Chongqing Technology and Business University (2023)
Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)
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)
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)
Yu, T.R., Jin, R., Han, X.Z.: Review of pre-training models for natural language processing. Comput. Eng. Appl. 56, 12–22 (2020)
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)
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)
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)
Chen, J.J.: Research on Target-level Sentiment Analysis of Texts Based on Deep Learning. National University of Defense Technology (2018)
Ma, N.: Research on Comment Text Sentiment Analysis and Interest Recommendation under Cross Domain. Liaoning Technical University (2023)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Zhao, C.Y.: Research on Aspect Level Sentiment Analysis Method that Merge Position Information and Opinion Span. Chongqing University of Posts and Telecommunications, (2022)
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)
He, Z.H., Chen, H.M.: Aspect based Sentiment Analysis is Based on Aspect Semantic and Gated Filtering Network. Southwest Jiaotong University (2023)
Shazeer, N.M., Lan, Z.Z., Cheng, Y.L.: Talking-Heads Attention. Arxiv (2020)
Lin, Z.C., Li, B.Z.: Aspect-based sentiment analysis based on local context focus mechanism and talking-head attention. Comput. Sci. (2022)
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)
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)
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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
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