Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
References
[1]
Phan, H.T., Nguyen, N.T. and Hwang, D. (2023) Aspect-Level Sentiment Analysis: A Survey of Graph Convolutional Network Methods. Information Fusion, 91, 149-172. https://doi.org/10.1016/j.inffus.2022.10.004
[2]
Ma, D., Li, S., Zhang, X. and Wang, H. (2017) Interactive Attention Networks for Aspect-Level Sentiment Classification. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, 19-25 August 2017, 4068-4074. https://doi.org/10.24963/ijcai.2017/568
[3]
Chen, P., Sun, Z., Bing, L. and Yang, W. (2017) Recurrent Attention Network on Memory for Aspect Sentiment Analysis. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, 7-11 September 2017, 452-461. https://doi.org/10.18653/v1/d17-1047
[4]
Fan, F., Feng, Y. and Zhao, D. (2018) Multi-Grained Attention Network for Aspect-Level Sentiment Classification. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, 31 October-4 November 2018, 3433-3442. https://doi.org/10.18653/v1/d18-1380
[5]
Kipf, T.N. and Welling, M. (2017) Semi-Supervised Classification with Graph Convolutional Networks. The 5th International Conference on Learning Representations, Toulon, 24-26 April 2017, 1-14.
[6]
Tang, D., Qin, B., Feng, X., et al. (2016) Effective LSTMs for Target-Dependent Sentiment Classification. Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, 11-17 December 2016, 3298-3307.
[7]
Wang, Y., Huang, M., Zhu, X. and Zhao, L. (2016) Attention-Based LSTM for Aspect-Level Sentiment Classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, 1-5 November 2016, 606-615. https://doi.org/10.18653/v1/d16-1058
[8]
Sun, K., Zhang, R., Mensah, S., Mao, Y. and Liu, X. (2019) Aspect-Level Sentiment Analysis via Convolution over Dependency Tree. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, 3-7 November 2019, 5679-5688. https://doi.org/10.18653/v1/d19-1569
[9]
He, R., Lee, W., Ng, H., et al. (2018) Effective Attention Modeling for Aspect-Level Sentiment Classification. Proceedings of the 27th International Conference on Computational Linguistics, Stroudsburg, 20-26 August 2018, 1121-1131.
[10]
Zhou, J., Huang, J.X., Hu, Q.V. and He, L. (2020) SK-GCN: Modeling Syntax and Knowledge via Graph Convolutional Network for Aspect-Level Sentiment Classification. Knowledge-Based Systems, 205, Article 106292. https://doi.org/10.1016/j.knosys.2020.106292
[11]
Liang, B., Su, H., Gui, L., Cambria, E. and Xu, R. (2022) Aspect-Based Sentiment Analysis via Affective Knowledge Enhanced Graph Convolutional Networks. Knowledge-Based Systems, 235, Article 107643. https://doi.org/10.1016/j.knosys.2021.107643
[12]
Huang, B. and Carley, K. (2019) Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, 3-7 November 2019, 5469-5477. https://doi.org/10.18653/v1/d19-1549
[13]
Zhang, C., Li, Q. and Song, D. (2019) Aspect-Based Sentiment Classification with Aspect-Specific Graph Convolutional Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, 3-7 November 2019, 4568-4578. https://doi.org/10.18653/v1/d19-1464
[14]
Zou, L., Xia, L., Ding, Z., Song, J., Liu, W. and Yin, D. (2019) Reinforcement Learning to Optimize Long-Term User Engagement in Recommender Systems. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, 4-8 August 2019, 2810-2818. https://doi.org/10.1145/3292500.3330668
[15]
Wang, K., Shen, W., Yang, Y., Quan, X. and Wang, R. (2020) Relational Graph Attention Network for Aspect-Based Sentiment Analysis. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5-10 July 2020, 3229-3238. https://doi.org/10.18653/v1/2020.acl-main.295
[16]
Zhang, M. and Qian, T. (2020) Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 5-10 July 2020, 3540-3549. https://doi.org/10.18653/v1/2020.emnlp-main.286
[17]
Wang, R., Tao, Z., Zhao, R., et al. (2022) Multi-Interaction Graph Convolutional Networks for Aspectual Sentiment Analysis. Journal of Electronics and Information, 44, 1111-1118.
[18]
Zhao, Z., Liu, Y., Gao, J., Wu, H., Yue, Z. and Li, J. (2022) Multi-Grained Syntactic Dependency-Aware Graph Convolution for Aspect-Based Sentiment Analysis. 2022 International Joint Conference on Neural Networks (IJCNN), Padua, 18-23 July 2022, 1-8. https://doi.org/10.1109/ijcnn55064.2022.9892472
[19]
Yuan, L., Wang, J., Yu, L. and Zhang, X. (2024) Syntactic Graph Attention Network for Aspect-Level Sentiment Analysis. IEEE Transactions on Artificial Intelligence, 5, 140-153. https://doi.org/10.1109/tai.2022.3227535
