Abstract
Sentiment classification which aims to predict a user’s sentiment about a product is becoming more and more useful and important. Some neural network methods achieved improvement by capturing user and product information. However, these methods fail to incorporate user preferences and product characteristics reasonably and effectively. What’s more, these methods all only use the explicit influences observed in texts and ignore the implicit interaction influences between user and product which cannot be observed in texts. In this paper, we propose a novel neural network model HUPSA-NCF (Hierarchical User Product Separated Attention and Neural Collaborative Filtering Network) to address these issues. Firstly, our model uses hierarchical user and product separated attention on BiLSTM to incorporate user preferences and product characteristics into specific text representations respectively. Secondly, our model uses neural collaborative filtering to capture the implicit interaction influences between user and product. Lastly, our model makes full use of both explicit and implicit informations for final classification. Experimental results show that our model outperforms state-of-the-art methods on IMDB and Yelp datasets.
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
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012). https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Mikolov, T., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP (2014). https://doi.org/10.3115/v1/D14-1181
Tang, D., Qin, B., Liu, T.: Document modelling with gated recurrent neural network for sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2015). https://doi.org/10.18653/v1/D15-1167
Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning, ICML (2011)
Zhang, X., et al.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, NIPS (2015)
Yang, Z., et al.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016). https://doi.org/10.18653/v1/N16-1174
Hochreiter, et al.: Long short-term memory. Neural Comput. 9(8) (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Bahdanau, D., et al.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations, ICLR (2015)
Tang, D., Qin, B., Liu, T.: Learning semantic representations of users and products for document level sentiment classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL&IJCNLP (2015). https://doi.org/10.3115/v1/P15-1098
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP (2016). https://doi.org/10.18653/v1/D16-1171
Dou, Z.-Y.: Capturing user and product information for document level sentiment analysis with deep memory network. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP (2017). https://doi.org/10.18653/v1/D17-1054
Wu, Z., Dai, X.-Y., Yin, C., Huang, S., Chen, J.: Improving review representations with user attention and product attention for sentiment classification. In: Thirty-Second AAAI Conference on Artificial Intelligence, AAAI (2018)
Manning, C., et al.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL (2014). https://doi.org/10.3115/v1/P14-5010
Kingma, D.P., et al.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations, ICLR (2015)
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
Yan, M., Wang, C., Sha, Y. (2019). Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Attention and Neural Collaborative Filtering. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-30490-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30489-8
Online ISBN: 978-3-030-30490-4
eBook Packages: Computer ScienceComputer Science (R0)