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Opinion Polarity Identification of Movie Reviews

  • Franco Salvetti
  • Christoph Reichenbach
  • Stephen Lewis
Chapter
Part of the The Information Retrieval Series book series (INRE, volume 20)

Abstract

One approach to the assessment of overall opinion polarity (OvOP) of reviews, a concept defined in this paper, is the use of supervised machine learning mechanisms. In this paper, the impact of lexical feature selection and feature generalization, applied to reviews, on the precision of two probabilistic classifiers (Naïve Bayes and Markov Model) with respect to OvOP identification is observed. Feature generalization based on hypernymy as provided by WordNet, and feature selection based on part-ofspeech (POS) tags are evaluated. A ranking criterion is introduced, based on a function of the probability of having positive or negative polarity, which makes it possible to achieve 100% precision with 10% recall. Movie reviews are used for training and testing the probabilistic classifiers, which achieve 80% precision.

Keywords

opinion polarity sentiment identification synonymy feature generalization hypernymy feature generalization POS feature selection probabilistic classification 

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Copyright information

© Springer 2006

Authors and Affiliations

  • Franco Salvetti
    • 1
  • Christoph Reichenbach
    • 1
  • Stephen Lewis
    • 2
  1. 1.Dept. of Computer ScienceUniversity of ColoradoBoulderUSA
  2. 2.Dept. of LinguisticsUniversity of ColoradoBoulderUSA

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