Volume 17 Language Research in the 21st Century
Graduate Working Papers

Automatic Opinion Polarity Classification of Movie Reviews

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  • Franco Salvetti,
  • Stephen Lewis,
  • Christoph Reichenbach
Franco Salvetti
University of Colorado Boulder
Stephen Lewis
University of Colorado Boulder
Christoph Reichenbach
University of Colorado Boulder
DOI: https://doi.org/10.25810/atv1-v819

Keywords

  • computational linguistics

How to Cite

Salvetti, F., Lewis, S., & Reichenbach, C. (2004). Automatic Opinion Polarity Classification of Movie Reviews. Colorado Research in Linguistics, 17. https://doi.org/10.25810/atv1-v819

Abstract

One approach to assessing 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 filtering, applied to reviews, on the accuracy of two statistical classifiers (Naive Bayes and Markov Model) with respect to OvOP identification is observed. Two kinds of lexical filters, one based on hypernymy as provided by WordNet (Fellbaum, 1998), and one hand-crafted filter based on part-of-speech (POS) tags, are evaluated. A ranking criterion based on a function of the probability of having positive or negative polarity is introduced and verified as being capable of achieving 100% accuracy with 10% recall. Movie reviews are used for training and evaluation of each statistical classifier, achieving 80% accuracy.

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