Abstract
Aspect-level sentiment classification, which aims to determine the sentiment polarity of the specific target word or phrase of a sentence, is a crucial task in natural language processing (NLP). Previous works have proposed various attention methods to capture the important part of the context for the desired target. However, these methods have less interaction between aspects and contexts and can not accurately quantify the importance of context words with the information of aspect. To address these issues, we firstly proposed a novel target-based attention model (TBAM) for aspect-level sentiment analysis, which employs an attention mechanism between the position-aware context representation matrix. TBAM can generate more accurate attention scores between aspects and contexts at the word level in a joint way, and generate more discriminative features for classification. Experimental results show that our model achieves a state-of-the-art performance on three public datasets compared to other architectures.
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
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent Twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers). ACL, Baltimore (2014)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING (2016)
Vo, D.-T., Zhang, Y.: Target-dependent Twitter sentiment classificaiton with rich automatic features. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, pp. 1347–1353 (2015)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: Proceedings of 26th IJCAI, Melbourne, Australia, pp. 4068–4074 (2017)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of EMNLP (2016)
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent Twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers). ACL, Baltimore (2014)
Kiritchenko, S., Zhu, X., Cherry, C., Mohammad, S.: Detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th International Workshop on Semantic Evaluation, pp. 437–442 (2014)
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)
Wang, Y., Huang, M., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151–160 (2011)
Wagner, J., et al.: DCU: aspect-based polarity classification for semeva task 4 (2014)
Pontiki, M., et al.: Semeval 2016 task 5: aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, vol. 333, pp. 2267–2273 (2015)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)
Cui, Y., Chen, Z., Wei, S., Wang, S., Liu, T., Hu, G.: Attention-over-attention neural networks for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 593–602 (2017)
Acknowledgement
This research was supported by 2018GZ0517, 2019YFS0146, 2019YFS0155, which supported by Sichuan Provincial Science and Technology Department, 2018KF003 Supported by State Key Laboratory of ASIC & System. No. 61907009 Supported by National Natural Science Foundation of China, No. 2018A030313802 Supported by Natural Science Foundation of Guangdong Province, No. 2017B010110007 and 2017B010110015 Supported by Science and Technology Planning Project of Guangdong Province.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, W. et al. (2019). Target-Based Attention Model for Aspect-Level Sentiment Analysis. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-36718-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36717-6
Online ISBN: 978-3-030-36718-3
eBook Packages: Computer ScienceComputer Science (R0)