Skip to main content
SpringerLink
Log in

Sentiment classification via user and product interactive modeling

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Sentiment classification aims to identify the polarity of a given review. Most existing methods consider each review as an individual while ignoring the importance of the user and product information of the given review. A direct way to integrate user and product information is to employ an attention mechanism to learn the local interaction between them. However, local interactions cannot capture the global optimization among user and product information. Therefore, we propose a novel interactive model to integrate both local and global interactions between users and products. In particular, we employ an attention mechanism to learn local interactions between users and products, and construct user and product interactive graphs to model the global interaction of users and products. Empirical evaluation shows that our model outperforms previous state-of-the-art methods significantly by learning the local and global interactions among users’ preferences, product characteristics, and reviews.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kapočiūtė-Dzikienė J, Damaševičius R, Woźniak M. Sentiment analysis of lithuanian texts using traditional and deep learning approaches. Computers, 2019, 8: 4

    Article  Google Scholar 

  2. Roy A, Guria S, Halder S, et al. Summarizing opinions with sentiment analysis from multiple reviews on travel destinations. Int J Synth Emotions, 2018, 9: 111–120

    Article  Google Scholar 

  3. Wan X. Co-training for cross-lingual sentiment classification. In: Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics, 2009. 235–243

  4. Kiritchenko S, Zhu X, Mohammad S M. Sentiment analysis of short informal texts. J Artif Intell Res, 2014, 50: 723–762

    Article  Google Scholar 

  5. Kalaivani P, Shunmuganathan K L. Feature selection based on genetic algorithm and hybrid model for sentiment polarity classification. Int J Data Mining Model Manag, 2016, 8: 315–329

    Google Scholar 

  6. Yang M, Tu W, Wang J, et al. Attention based LSTM for target dependent sentiment classification. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017. 5013–5014

  7. Chen T, Xu R, He Y, et al. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst Appl, 2017, 72: 221–230

    Article  Google Scholar 

  8. Chen H, Sun M, Tu C, et al. Neural sentiment classification with user and product attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. 1650–1659

  9. Wu Z, Dai X, Yin C, et al. Improving review representations with user attention and product attention for sentiment classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018. 5989–5996

  10. Kim J, Amplayo R K, Lee K, et al. Categorical metadata representation for customized text classification. Trans Assoc Comput Linguist, 2019, 7: 201–215

    Article  Google Scholar 

  11. Liu S H, Li F X, Li F T, et al. Adaptive co-training SVM for sentiment classification on tweets. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013. 2079–2088

  12. Zhang D, Xu H, Su Z, et al. Chinese comments sentiment classification based on word2vec and SVMperf. Expert Syst Appl, 2015, 42: 1857–1863

    Article  Google Scholar 

  13. Song J, Kim K T, Lee B J, et al. A novel classification approach based on Naïve Bayes for Twitter sentiment analysis. KSII TIIS, 2017, 11: 2996–3011

    Google Scholar 

  14. Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, 2014. 1746–1751

  15. Wei X, Lin H, Yu Y, et al. Low-resource cross-domain product review sentiment classification based on a CNN with an auxiliary large-scale corpus. Algorithms, 2017, 10: 81

    Article  Google Scholar 

  16. Nguyen T L, Kavuri S S, Lee M. A fuzzy convolutional neural network for text sentiment analysis. J Intell Fuzzy Syst, 2018, 35: 6025–6034

    Article  Google Scholar 

  17. Liu F, Zheng J, Zheng L, et al. Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification. Neurocomputing, 2020, 371: 39–50

    Article  Google Scholar 

  18. 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, Lisbon, 2015. 1422–1432

  19. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9: 1735–1780

    Article  Google Scholar 

  20. Wang X, Liu Y, Sun C, et al. Predicting polarities of tweets by composing word embeddings with long short-term memory. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, 2015. 1343–1353

  21. Qian Q, Huang M, Lei J, et al. Linguistically regularized LSTM for sentiment classification. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017. 1679–1689

  22. Lei Z, Yang Y, Yang M. Sentiment lexicon enhanced attention-based LSTM for sentiment classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018. 8105–8106

  23. Feng S, Wang Y, Liu L, et al. Attention based hierarchical LSTM network for context-aware microblog sentiment classification. World Wide Web, 2019, 22: 59–81

    Article  Google Scholar 

  24. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 5998–6008

  25. Long F, Zhou K, Ou W. Sentiment analysis of text based on bidirectional LSTM with multi-head attention. IEEE Access, 2019, 7: 141960–141969

    Article  Google Scholar 

  26. Devlin J, Chang M, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding. 2018. ArXiv:1810.04805

  27. Mathew L, Bindu V R. A review of natural language processing techniques for sentiment analysis using pre-trained models. In: Proceedings of the 4th International Conference on Computing Methodologies and Communication (ICCMC), 2020. 340–345

  28. Hao Y, Dong L, Wei F, et al. Self-attention attribution: interpreting information interactions inside transformer. 2020. ArXiv:2004.11207

  29. 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, 2015. 1014–1023

  30. Gui L, Zhou Y, Xu R, et al. Learning representations from heterogeneous network for sentiment classification of product reviews. Knowl-Based Syst, 2017, 124: 34–45

    Article  Google Scholar 

  31. Ma D, Li S, Zhang X, et al. Cascading multiway attentions for document-level sentiment classification. In: Proceedings of the 8th International Joint Conference on Natural Language Processing, 2017. 634–643

  32. Dou Z. 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, 2017. 521–526

  33. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of International Conference on Learning Representations, 2017

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61525205, 61702518).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Xiabing Zhou, Zhongqing Wang, Min Zhou, Qifa Wang, Shoushan Li, Min Zhang & Guodong Zhou

Authors
  1. Xiabing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qifa Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shoushan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guodong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongqing Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, X., Wang, Z., Zhou, M. et al. Sentiment classification via user and product interactive modeling. Sci. China Inf. Sci. 64, 222104 (2021). https://doi.org/10.1007/s11432-020-3116-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3116-x

Keywords

Search

Navigation