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)


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.


Sentiment Analysis Review Ranking Ordinal Regression model SVM Ranking 


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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

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