Advertisement

Automatically Ranking Reviews Based on the Ordinal Regression Model

  • Bing Xu
  • Tie-Jun Zhao
  • Jian-Wei Wu
  • Cong-Hui Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7004)

Abstract

With the rapid development of Internet and E-commerce, the quantity of product reviews on the web grows very fast, but the review quality is inconsistent. This paper addresses the problem of automatically ranking reviews. A specification for judging the reviews quality is first defined and thus ranking review is formalized as ordinal regression problem. In this paper, we employ Ranking SVM as the ordinal regression model. To improve system performance, we capture many important features, including structural features, syntactic features and semantic features. Experimental results indicate that Ranking SVM can obviously outperform baseline methods. For the identification of low-quality reviews, the Ranking SVM model is more effective than SVM regression model. Experimental results also show that the unigrams, adjectives and product features are more effective features for modeling.

Keywords

Sentiment Analysis Review Ranking Ordinal Regression model SVM Ranking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automacically Assessing Review Helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 423–430 (2006)Google Scholar
  2. 2.
    Liu, J.J., Cao, Y.B., Li, C.Y., Huang, Y.L., Zhou, M.: Low-Quality Product Review Detection in Opinion Summarization. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 334–342 (2007)Google Scholar
  3. 3.
    Zhang, R.C., Thomas, T.: Helpful or Unhelpful: A Linear Approach for Ranking Product Reviews. Journal of Electronic Commerce Research 11(3), 220–230 (2010)Google Scholar
  4. 4.
    Zhang, Z., Varadarajan, B.: Utility Scoring of Product Reviews. In: CIKM 2006, pp. 52–57 (2006)Google Scholar
  5. 5.
    Liu, Y., Huang, X., An, A., Yu, X.: Modeling and Predicting the Helpfulness of Online Reviews. In: Proceedings of the 8th International Conference on Data Mining, pp. 443–452 (2008)Google Scholar
  6. 6.
    Lim, E.-P., Nguyen, V.-A., Jindal, N., et al.: Detecting product review spammers using rating behaviors. In: Proceedings of the International Conference on information and Knowledge Management (2010)Google Scholar
  7. 7.
    Lei, Z., Bing, L., Hwan, L.S., O’Brien-Strain, E.: Extacting and Ranking Product Features in Opinion Documents. In: Coling 2010, pp. 1462–1470 (2010)Google Scholar
  8. 8.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification Using Machine Learning Techniques. In: EMNLP 2002, pp. 79–86 (2002)Google Scholar
  9. 9.
    Turney, P.: Thumbs up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: ACL 2002, pp. 417–424 (2002)Google Scholar
  10. 10.
    Liu, B., Hu, M., Cheng, J.: Opinion Observer: Analyzing and Comparing Opinions on the Web. In: Proc. International World Wide Web Conference, pp. 342–351 (May 2005)Google Scholar
  11. 11.
    Popescu, A.M., Etzioni, O.: Extracting Product Features and Opinions from Reviews. In: Proceedings of Empirical Methods in Natural Language Processing, pp. 339–346 (2005)Google Scholar
  12. 12.
    Xu, J., Cao, Y.-B., Li, H., Zhao, M., Huang, Y.-L.: A supervised Learning Approach to Search of Definations. Journal of Computer Science and Technology 21(3), 439–449 (2006)CrossRefGoogle Scholar
  13. 13.
    Liu, T.-Y.: Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval 3(3), 225–331 (2009)CrossRefGoogle Scholar
  14. 14.
    Herbrich, R., Graepel, T., Obermayer, K.: Suppert vector learning for ordinal regression. In: Proceedings of 9th International Conference Artificial Neural Networks, pp. 97–102 (1999)Google Scholar
  15. 15.
    Joachims, T.: Optimizing Search Engines Using Click-through Data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bing Xu
    • 1
  • Tie-Jun Zhao
    • 1
  • Jian-Wei Wu
    • 1
  • Cong-Hui Zhu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations