Paper: Assigning Polarity Scores to Reviews Using Machine Learning Techniques

ACL ID I05-1028
Title Assigning Polarity Scores to Reviews Using Machine Learning Techniques
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors
  • Daisuke Okanohara (University of Tokyo, Tokyo Japan)
  • Jun'ichi Tsujii (University of Tokyo, Tokyo Japan; University of Manchester, Manchester UK; CREST Japan Science and Technology Corporation, Saitama Japan)

We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but a continuous mea- sure called sentiment polarity score (sp-score). An sp-score gives a very concise summary of a review and provides more information than binary classification. The difficulty of this task lies in the quantification of po- larity. In this paper we use support vector regression (SVR) to tackle the problem. Experiments on book reviews with five-point scales show that SVR outperforms a multi-class classification method using support vector machines and the results are close to human performance.