Aspect-Level Sentiment Analysis with Multi-Channel and Graph Convolutional Networks

Authors: Jiajun Wang, Xiaoge Li

Abstract:

The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose an aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: Aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree.

References:

[1] E. Burnaey, D. Smolyakoy, “One-Class SVM with Privileged Information and its Application to Malware Detection,” IEEE (2016).
[2] L. H. Zhang, et al. “Factors affecting decision tree classification method over TM image.” in Forest Research, Beijing, pp. 1-5, 2014.
[3] S. Mishra, M. Panda, “Histogram of oriented gradients-based digit classification using naive Bayesian classifier.” in Progress in Computing, Analytics and Networking, Springer, Singapore, pp. 285-294.
[4] Y. Wang, M. Huang, X. Zhu, and L. Zhao, “Attention-based LSTM for aspect-level sentiment classification,” in Proceedings of the 2016 conference on empirical methods in natural language processing, pp. 606-615, Nov 2016.
[5] D. Tang, B. Qin, X. Feng, and T. Liu, “Effective LSTMs for Target-Dependent Sentiment Classification.” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307, 2016.
[6] Sun, L. Bing, and W. Yang, “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, Sep 2017.
[7] F. Fan, Y. Feng, and D. Zhao, “Multi-grained attention network for aspect-level sentiment classification.” in Proceedings of the 2018 conference on empirical methods in natural language processing, pp. 3433-3442, 2018.
[8] D. Ma, S. Li, X. Zhang, and H. Wang, “Interactive attention networks for aspect-level sentiment classification.” in Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4068–4074, 2017.
[9] R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier, “Effective attention modeling for aspect-level sentiment classification.” in Proceedings of the 27th international conference on computational linguistics, pp. 1121-1131, Aug 2018.
[10] S. Gu, Zhang, L. Zhang, Y. Hou, and Y. Song, “A position-aware bidirectional attention network for aspect-level sentiment analysis.” in Proceedings of the 27th international conference on computational linguistics, pp. 774-784, Aug 2018.
[11] R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier, “Exploiting document knowledge for aspect-level sentiment classification.” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2018.
[12] Y. Ma, H. Peng, and E. Cambria, "Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM.” AAAI 2018.
[13] T. N. Kip F, M. Welling, “Semi-supervised classification with graph convolutional networks.” 2016.
[14] K. Sun, Zhang, R. Zhang, S. Mensah, Y. Mao, and X. Liu, “Aspect-level sentiment analysis via convolution over dependency tree.” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5679-5688, Nov 2019.
[15] B. Huang, K. M. Carley, “Syntax-aware aspect level sentiment classification with graph attention networks.” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5472–5480 2019.
[16] C. Zhang, Q. Li, D. Song, “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 (EMNLP-IJCNLP), pp. 4560-4570, 2019.
[17] K. Wang, W. Shen, Y. Yang, X. Quan, and R. Wang, “Relational Graph Attention Network for Aspect-based Sentiment Analysis.” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238, Online 2020.
[18] L. Xiao, X. Hu, Y. Chen, Y. Xue, and T. Zhang, “Targeted sentiment classification based on attentional encoding and graph convolutional networks.” Applied Sciences, 10(3), 957, 2020.
[19] B. Huang, K.M. Carley, “Parameterized convolutional neural networks for aspect level sentiment classification” 2019.
[20] Z. Chen, T. Qian, “Transfer capsule network for aspect level sentiment classification.” in Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 547-556, July 2019.
[21] C. Du, H. Sun, J. Wang, Q. Qi, and M. Liu, “Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification.” in 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.
[22] C. Chen, Z. Teng, and Y. Zhang, “Inducing target-specific latent structures for aspect sentiment classification.” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online. Association for Computational Linguistics (2020), pp. 5596–5607, 2020.
[23] M. Pontiki, et al., “SemEval-2016 Task 5: Aspect Based Sentiment Analysis.” in International Workshop on Semantic Evaluation 2018.
[24] M. Pontiki, et al., “SemEval-2014 Task 4: Aspect Based Sentiment Analysis. in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval-2014).
[25] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, K. Xu, “Adaptive recursive neural network for target-dependent twitter sentiment classification.” In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 49–54. Baltimore, MD, USA, 23–25 Jun 2014.