Review Comment Analysis for Predicting Ratings

  • Rong Zhang
  • Yifan Gao
  • Wenzhe Yu
  • Pingfu Chao
  • Xiaoyan Yang
  • Ming GaoEmail author
  • Aoying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)


Rating prediction is a common task in recommendation systems that aims to predict a rating representing the opinion from a user to an item. In this paper, we propose a comment-based collaborative filtering (CCF) approach that captures correlations between hidden aspects in review comments and numeric ratings. The idea is motivated by the observation that the opinion of a user against an item is represented by different aspects discussed in review comments. In our approach, we first explores topic modeling to discover hidden aspects from review comments. Profiles are then created for users and items separately based on the discovered aspects. In the testing stage, we estimate the aspects of comments based on the profiles of users and items because the comments are not available when testing. Lastly, we build final systems by utilizing the profiles and traditional collaborative filtering methods. We evaluate the proposed approach on a real data set. The experimental results show that our prediction systems outperform several strong baseline systems.


Random Forest Recommender System Latent Dirichlet Allocation Mean Absolute Error Online Review 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blanco-Fernández, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., López-Nores, M., García-Duque, J., Fernández-Vilas, A., Díaz-Redondo, R.P., Bermejo-Muñoz, J.: A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowledge-Based Systems 21(4), 305–320 (2008)CrossRefGoogle Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of machine Learning research 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Machine learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Brody, S., Elhadad, N.: 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, pp. 804–812. Association for Computational Linguistics (2010)Google Scholar
  5. 5.
    Freedman, D.: Statistical models: theory and practice. Cambridge University Press (2009)Google Scholar
  6. 6.
    Friedman, J.H.: Stochastic gradient boosting. Computational Statistics & Data Analysis 38(4), 367–378 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Ganu, G., Elhadad, N., Marian, A.: Beyond the stars: improving rating predictions using review text content. In: WebDB (2009)Google Scholar
  8. 8.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)Google Scholar
  9. 9.
    Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 815–824. ACM (2011)Google Scholar
  10. 10.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 145–186. Springer (2011)Google Scholar
  11. 11.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  12. 12.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering (2005)Google Scholar
  13. 13.
    McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 165–172. ACM (2013)Google Scholar
  14. 14.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and trends in information retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  15. 15.
    Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Natural language Processing and Text Mining, pp. 9–28. Springer (2007)Google Scholar
  16. 16.
    Qu, L., Ifrim, G., Weikum, G.: The bag-of-opinions method for review rating prediction from sparse text patterns. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 913–921. Association for Computational Linguistics (2010)Google Scholar
  17. 17.
    Rajaraman, A., Ullman, J.D.: Mining of massive datasets, Chapter 9. Cambridge University Press (2012)Google Scholar
  18. 18.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  19. 19.
    Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. Urbana 51, 61801 (2008)Google Scholar
  20. 20.
    Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rong Zhang
    • 1
    • 2
  • Yifan Gao
    • 1
    • 2
  • Wenzhe Yu
    • 1
    • 2
  • Pingfu Chao
    • 1
    • 2
  • Xiaoyan Yang
    • 3
  • Ming Gao
    • 1
    • 2
    Email author
  • Aoying Zhou
    • 1
    • 2
  1. 1.Institute for Data Science and EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Trustworthy ComputingEast China Normal UniversityShanghaiChina
  3. 3.Advanced Digital Sciences CenterIllinois at Singapore Pte. Ltd.SingaporeSingapore

Personalised recommendations