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Opinion Polarity Identification of Movie Reviews

Salvetti, Franco ; Reichenbach, Christoph LU orcid and Lewis, Stephen (2006) p.303-316
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Computing Attitude and Affect in Text: Theory and Applications
editor
Shanahan, James G. ; Qu, Yan and Wiebe, Janyce
article number
Chapter 23
pages
303 - 316
publisher
Springer
ISBN
1-4020-4026-1
DOI
10.1007/1-4020-4102-0_23
language
English
LU publication?
no
id
60d66e13-089b-4b51-8dca-65d908e171e1
date added to LUP
2019-03-29 20:30:42
date last changed
2025-04-04 15:26:32
@inbook{60d66e13-089b-4b51-8dca-65d908e171e1,
  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.}},
  author       = {{Salvetti, Franco and Reichenbach, Christoph and Lewis, Stephen}},
  booktitle    = {{Computing Attitude and Affect in Text: Theory and Applications}},
  editor       = {{Shanahan, James G. and Qu, Yan and Wiebe, Janyce}},
  isbn         = {{1-4020-4026-1}},
  language     = {{eng}},
  pages        = {{303--316}},
  publisher    = {{Springer}},
  title        = {{Opinion Polarity Identification of Movie Reviews}},
  url          = {{http://dx.doi.org/10.1007/1-4020-4102-0_23}},
  doi          = {{10.1007/1-4020-4102-0_23}},
  year         = {{2006}},
}