Abstract
Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.
Y. Li, C. Yin—Equal contribution.
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
Cheng, J., Zhao, S., Zhang, J., King, I., Zhang, X., Wang, H.: Aspect-level sentiment classification with heat (hierarchical attention) network. In: CIKM, pp. 97–106 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hu, M., et al.: Can: constrained attention networks for multi-aspect sentiment analysis. In: EMNLP-IJCNLP, pp. 4593–4602 (2019)
Jiang, Q., Chen, L., Xu, R., Ao, X., Yang, M.: A challenge dataset and effective models for aspect-based sentiment analysis. In: EMNLP-IJCNLP, pp. 6281–6286 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Li, C., Guo, X., Mei, Q.: Deep memory networks for attitude identification. In: WSDM, pp. 671–680 (2017)
Liang, B., Du, J., Xu, R., Li, B., Huang, H.: Context-aware embedding for targeted aspect-based sentiment analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 4678–4683. Association for Computational Linguistics, July 2019
Liang, Y., Meng, F., Zhang, J., Xu, J., Chen, Y., Zhou, J.: A novel aspect-guided deep transition model for aspect based sentiment analysis. In: EMNLP-IJCNLP, pp. 5572–5584 (2019)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retrieval 2(1–2), 1–135 (2008)
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 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)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014)
Ruder, S., Ghaffari, P., Breslin, J.G.: A hierarchical model of reviews for aspect-based sentiment analysis. In: EMNLP, pp. 999–1005 (2016)
Saeidi, M., Bouchard, G., Liakata, M., Riedel, S.: SentiHood: targeted aspect based sentiment analysis dataset for urban neighbourhoods. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1546–1556. The COLING 2016 Organizing Committee, Osaka, Japan, December 2016
Schmitt, M., Steinheber, S., Schreiber, K., Roth, B.: Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks. In: EMNLP, pp. 1109–1114 (2018)
Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. NAACL 1, 380–385 (2019)
Tay, Y., Tuan, L.A., Hui, S.C.: Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In: AAAI (2018)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)
Wang, Y., Sun, A., Huang, M., Zhu, X.: Aspect-level sentiment analysis using as-capsules. In: The World Wide Web Conference, pp. 2033–2044 (2019)
Xing, B., et al.: Earlier attention? aspect-aware LSTM for aspect sentiment analysis. arXiv preprint arXiv:1905.07719 (2019)
Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. In: ACL (Volume 1: Long Papers), pp. 2514–2523 (2018)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: NAACL, pp. 1480–1489 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Y. et al. (2020). Better Queries for Aspect-Category Sentiment Classification. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-63031-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63030-0
Online ISBN: 978-3-030-63031-7
eBook Packages: Computer ScienceComputer Science (R0)