Impact of lexical filtering on overall opinion polarity identification

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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 Word-Net (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.

Details

Authors
External organisations
  • University of Colorado
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Language Technology (Computational Linguistics)
Original languageEnglish
Title of host publicationEXPLORING ATTITUDE AND AFFECT IN TEXT: THEORIES AND APPLICATIONS
Subtitle of host publicationPapers from the AAAI Spring Symposium
PublisherAAAI Press
Pages128-133
Number of pages6
ISBN (Print) 978-1-57735-219-8
Publication statusPublished - 2005 Dec 1
Publication categoryResearch
Peer-reviewedYes
Externally publishedYes
Event2004 AAAI Spring Symposium - Stanford, CA, United States
Duration: 2004 Mar 222004 Mar 24

Conference

Conference2004 AAAI Spring Symposium
CountryUnited States
CityStanford, CA
Period2004/03/222004/03/24