Automatic Opinion Polarity Classification of Movie Reviews

Franco Salvetti, Stephen Lewis, and Christoph Reichenbach

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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.

Franco Salvetti is an MA student in the Department of Computer Science, Stephen Lewis is an MA student in the Department of Linguistics, and Christoph Reichenbach is a PhD student in the Department of Computer Science at the University of Colorado.

Colorado Research in Linguistics - Volume 17, Issue 1 - June 2004