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Review rating prediction using combined latent topics and associated sentiments: an empirical review

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Abstract

Understanding the tastes of each user and the characteristics of each product is necessary to predict how a user will respond to a new product. These latent user and product dimensions can be discovered with the help of user feedback. A numeric rating and its accompanying text review is the most widely available form of user feedback. A measure which encapsulates the contents of such reviews is often necessary as they have been found to significantly influence the shopping behavior of users. A fine-grained form of such measure that could act as a perfect feedback about the product is star rating. The review rating prediction tries to predict a rating corresponding to the given review. An approach that performs review rating prediction task by using the latent topics extracted from reviews and their associated sentiments is proposed in this paper. The proposed approach treats review rating prediction problem as a multi-class classification problem. An empirical review of topic modeling techniques such as (i) term frequency-inverse document frequency(TF-IDF), (ii) latent Dirichlet allocation(LDA), and (iii) nonnegative matrix factorization(NNMF) is performed in this work to investigate their efficiency. The proposed approach has a lot of advantages: Firstly, this approach accurately predicts product ratings by making use of the topics and their sentiments present in the reviews; this is useful in occasions where only reviews are available. Secondly, the discovered topics and their sentiments can be used to recognize informative reviews. Thirdly, it facilitates to justify the rating with review text. The experimental results show that the proposed model works well with latent topics and sentiments when the underlying model is trained using a deep learning technique.

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References

  1. Arora S, Ge R, Moitra A (2012) Learning topic models—going beyond svd. In: 2012 IEEE 53rd annual symposium on foundations of computer science, IEEE, pp 1–10

  2. Asghar N (2016) Yelp dataset challenge: review rating prediction. arXiv preprint arXiv:1605.05362

  3. Baccianella S, Esuli A, Sebastiani F (2009) Multi-facet rating of product reviews. In: European conference on information retrieval, Springer, pp 461–472

  4. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    MATH  Google Scholar 

  5. Brody S, Elhadad N (2010) An unsupervised aspect-sentiment model for online reviews. In: Human language technologies: the 2010 annual conference of the north American chapter of the association for computational linguistics, Association for Computational Linguistics, pp 804–812

  6. Catherine R, Cohen W (2017) Transnets: learning to transform for recommendation. In: Proceedings of the eleventh ACM conference on recommender systems, ACM, pp 288–296

  7. Chen L, Chen G, Wang F (2015) Recommender systems based on user reviews: the state of the art. User Model User Adapt Interact 25(2):99–154

    Article  Google Scholar 

  8. Christakopoulou E, Karypis G (2016) Local item-item models for top-n recommendation. In: Proceedings of the 10th ACM conference on recommender systems, ACM, pp 67–74

  9. Cosma G, Acampora G (2016) A computational intelligence approach to efficiently predicting review ratings in e-commerce. Appl Soft Comput 44:153–162

    Article  Google Scholar 

  10. Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, ACM, pp 191–198

  11. Ganu G, Elhadad N, Marian A (2009) Beyond the stars: improving rating predictions using review text content. In: WebDB, Citeseer, vol 9, pp 1–6

  12. Gaudette L, Japkowicz N (2009) Evaluation methods for ordinal classification. In: Canadian conference on artificial intelligence, Springer, pp 207–210

  13. He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 355–364

  14. He X, Chen T, Kan MY, Chen X (2015) Trirank: review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, pp 1661–1670

  15. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on World Wide Web, international World Wide Web conferences steering committee, pp 173–182

  16. Hong L, Davison BD (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics, ACM, pp 80–88

  17. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 168–177

  18. Hutto CJ, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth international AAAI conference on weblogs and social media

  19. Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook, Springer, pp 77–118

  20. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 8:30–37

    Article  Google Scholar 

  21. Lei X, Qian X, Zhao G (2016) Rating prediction based on social sentiment from textual reviews. IEEE Trans Multimed 18(9):1910–1921

    Article  Google Scholar 

  22. Lerman K, Blair-Goldensohn S, McDonald R (2009) Sentiment summarization: evaluating and learning user preferences. In: Proceedings of the 12th conference of the European chapter of the association for computational linguistics, Association for Computational Linguistics, pp 514–522

  23. Li D, Chen C, Lv Q, Shang L, Chu SM, Zha H (2017) Ermma: expected risk minimization for matrix approximation-based recommender systems. In: Thirty-first AAAI conference on artificial intelligence

  24. Liang Y (2017) Yelp rating prediction with sentiment and topic models. A master’s paper, School of Information and Library Science

  25. McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems, ACM, pp 165–172

  26. McAuley J, Leskovec J, Jurafsky D (2012) Learning attitudes and attributes from multi-aspect reviews. In: 2012 IEEE 12th international conference on data mining, IEEE, pp 1020–1025

  27. Popescu AM, Nguyen B, Etzioni O (2005) Opine: extracting product features and opinions from reviews. In: Proceedings of HLT/EMNLP 2005 interactive demonstrations

  28. Sedhain S, Menon AK, Sanner S, Xie L, Braziunas D (2017) Low-rank linear cold-start recommendation from social data. In: Thirty-first AAAI conference on artificial intelligence

  29. Sharma A, Cosley D (2013) Do social explanations work?: studying and modeling the effects of social explanations in recommender systems. In: Proceedings of the 22nd international conference on World Wide Web, ACM, pp 1133–1144

  30. Titov I, McDonald R (2008) A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of ACL-08: HLT, pp 308–316

  31. Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 783–792

  32. Wang X, He X, Feng F, Nie L, Chua TS (2018) Tem: Tree-enhanced embedding model for explainable recommendation. In: Proceedings of the 2018 World Wide Web conference on World Wide Web, international World Wide Web conferences steering committee, pp 1543–1552

  33. Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the tenth acm international conference on web search and data mining, ACM, pp 425–434

  34. Zheng X, He W, Li L (2019) Distributed representations based collaborative filtering with reviews. Appl Intell 49(7):2623–2640

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Saranathan College of Engineering, Trichy, Tamil Nadu, India

    Anbazhagan Mahadevan

  2. Department of Computer Applications, National Institute of Technology, Trichy, Tamil Nadu, India

    Michael Arock

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  1. Anbazhagan Mahadevan
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  2. Michael Arock
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Correspondence to Anbazhagan Mahadevan.

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Mahadevan, A., Arock, M. Review rating prediction using combined latent topics and associated sentiments: an empirical review. SOCA 14, 19–34 (2020). https://doi.org/10.1007/s11761-019-00278-6

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  • DOI: https://doi.org/10.1007/s11761-019-00278-6

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