Abstract
Aspect-level sentiment analysis, as an important type of sentiment analysis, is a fine-grained sentiment analysis task which has received much attention recently. Recent work combines attention mechanisms with neural networks to learn aspects feature and achieves state-of-the-art performance. However, the prior work ignores the sentiment terms feature and the latent correlation between aspect terms and sentiment terms. In order to make use of aspects terms and sentiment terms information, a method that based on joint attention LSTM network (JAT-LSTM) for aspect-level sentiment analysis is proposed, which aspect attention and sentiment attention are combined to construct a joint attention LSTM network. The experimental results on the benchmark datasets show that the proposed method achieves better performance than the current state-of-the-art.
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
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retr. 2(1–2), 1–135 (2008)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Yin, W., Schütze, H., Xiang, B., et al.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. Computer Science (2015)
Lample, G., Ballesteros, M., Subramanian, S., et al.: Neural architectures for named entity recognition, pp. 260–270. arXiv:1603.01360 (2016)
Golub, D., He, X.: Character-level question answering with attention. arXiv:1604.00727 [cs.CL] (2016)
Li, Z., Zhang, Y., Wei, Y., et al.: End-to-end adversarial memory network for cross-domain sentiment classification. In: Proceedings of the International Joint Conference on Artificial Intelligence (2017)
Mnih, V., Heess, N., Graves, A.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems, pp. 2204–2212 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014)
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. Computer Science (2015)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM), pp. 168–177 (2004)
Yu, J., Zha, Z.J., Wang, M., et al.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1496–1505. Association for Computational Linguistics (2011)
Nguyen, T.H., Shirai K.: Phrasernn: phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (2015)
Tang, D., Qin, B., Feng, X., et al.: Target-dependent sentiment classification with long short term memory. CoRR, abs/1512.01100 (2015)
Lakkaraju, H., Socher, R., Manning, C.: Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS Workshop on Deep Learning and Representation Learning (2014)
Liang, B., Liu, Q., Xu, J., et al.: Aspect-based sentiment analysis based on multi-attention CNN. J. Comput. Res. Dev. 54(8), 1724–1735 (2017)
Wang, Y., Huang, M., 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)
Cheng, J., Zhao, S., Zhang, J., et al.: Aspect-level sentiment classification with HEAT (HiErarchical ATtention) network. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM), pp. 97–106 (2017)
Acknowledgements
This work is supported by Chinese National Science Foundation (#61763007), Guangxi Natural Scicence Foundation (#2017JJD160017) and Innovation Project of GUET Graduate Education (#2018YJCX41).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Cai, G., Li, H. (2018). Joint Attention LSTM Network for Aspect-Level Sentiment Analysis. In: Zhang, S., Liu, TY., Li, X., Guo, J., Li, C. (eds) Information Retrieval. CCIR 2018. Lecture Notes in Computer Science(), vol 11168. Springer, Cham. https://doi.org/10.1007/978-3-030-01012-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-01012-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01011-9
Online ISBN: 978-3-030-01012-6
eBook Packages: Computer ScienceComputer Science (R0)