Opinion Polarity Identification of Movie Reviews

Research output: Chapter in Book/Report/Conference proceedingBook chapter

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.

Details

Authors
External organisations
  • University of Colorado
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Language Technology (Computational Linguistics)
Original languageEnglish
Title of host publicationComputing Attitude and Affect in Text: Theory and Applications
EditorsJames G. Shanahan, Yan Qu, Janyce Wiebe
PublisherSpringer
Pages303-316
ISBN (Print)1-4020-4026-1
Publication statusPublished - 2006
Publication categoryResearch
Peer-reviewedNo
Externally publishedYes