Abstract
Aspect-Level Sentiment Classification aims to identify the sentiment polarity of each aspect in a sentence. Syntax-based graph neural networks have been used to model dependencies between opinion words and aspects with good results. However, the analysis of these works is highly dependent on the quality of the dependency graph and may achieve suboptimal results for comments with ambiguous syntax. We explore a novel Multi-View Gated Graph Convolutional Network (MGGCN) to address the above problems. We utilize a Gated Graph Convolutional Network (GateGCN) for a more reasonable interaction of syntactic dependencies and semantic information, where we refine our syntactic dependency graph by adding sentiment knowledge and aspect-aware information to the dependency tree. We use the Inter-aspect Graph Convolutional Network (InterGCN) to capture information about the sentiment dependencies between multiple aspects that appear in a sentence. Finally, by adaptively learning multi-view sentiment information through Simple Residual Multilayer Perceptron(SResMLP). Experimental results on four public datasets show that our proposed model outperforms state-of-the-art models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bai, X., Liu, P., Zhang, Y.: Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 503–514 (2020)
Cambria, E., Poria, S., Hazarika, D., Kwok, K.: Senticnet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Chen, C., Teng, Z., Zhang, Y.: Inducing target-specific latent structures for aspect sentiment classification. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5596–5607 (2020)
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)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Fan, F., Feng, Y., Zhao, D.: 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)
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Li, J., Hovy, E.: Reflections on sentiment/opinion analysis. In: Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A. (eds) A Practical Guide to Sentiment Analysis. Socio-Affective Computing, vol 5. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55394-8_3
Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., Hovy, E.: 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 (vol. 1: Long Papers), pp. 6319–6329 (2021)
Liang, B., Su, H., Gui, L., Cambria, E., Xu, R.: Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl.-Based Syst. 235, 107643 (2022)
Liang, B., Yin, R., Gui, L., Du, J., Xu, R.: Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 150–161 (2020)
Lin, P., Yang, M., Lai, J.: Deep selective memory network with selective attention and inter-aspect modeling for aspect level sentiment classification. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 1093–1106 (2021)
Lu, G., Li, J., Wei, J.: Aspect sentiment analysis with heterogeneous graph neural networks. Inf. Process. Manage. 59(4), 102953 (2022)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893 (2017)
Majumder, N., Poria, S., Gelbukh, A., Akhtar, M.S., Cambria, E., Ekbal, A.: IARM: inter-aspect relation modeling with memory networks in aspect-based sentiment analysis. In: Proceedings of the 2018 Conference on Empirical Methods In Natural Language Processing, pp. 3402–3411 (2018)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of International Workshop on Semantic Evaluation (2014)
Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis. In: International Workshop on Semantic Evaluation, pp. 19–30 (2016)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015)
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108, 42–49 (2016)
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. arXiv preprint arXiv:1908.11860 (2019)
Song, Y., Wang, J., Jiang, T., Liu, Z., Rao, Y.: Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314 (2019)
Sun, K., Zhang, R., Mensah, S., Mao, Y., Liu, X.: 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 (2019)
Tang, D., et al.: Learning sentiment-specific word embedding for twitter sentiment classification. In: ACL (1), pp. 1555–1565. CiteSeer (2014)
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)
Touvron, H., et al.: ResMLP: feedforward networks for image classification with data-efficient training. arXiv preprint arXiv:2105.03404 (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. arXiv preprint arXiv:2004.12362 (2020)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016)
Xu, H., Liu, B., Shu, L., Yu, P.S.: Bert post-training for review reading comprehension and aspect-based sentiment analysis. arXiv preprint arXiv:1904.02232 (2019)
Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. arXiv preprint arXiv:1805.07043 (2018)
Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019)
Acknowledgements
The work is supported partly by National Natural Science Foundation of China (No. 62166003), the Innovation Project of Guangxi Graduate Education (YCSW-2022124),the Project of Guangxi Science and Technology (GuiKeAD20159041), Intelligent Processing and the Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (No.20–A–01–01, MIMS20–M–01), the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and the Guangxi “Bagui” Teams for Innovation and Research, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, L., Zhang, G., Lei, Z., Huang, Z., Lu, G. (2022). Multi-View Gated Graph Convolutional Network for Aspect-Level Sentiment Classification. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-22064-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22063-0
Online ISBN: 978-3-031-22064-7
eBook Packages: Computer ScienceComputer Science (R0)