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Impact of lexical filtering on overall opinion polarity identification

Salvetti, Franco ; Lewis, Stephen and Reichenbach, Christoph LU orcid (2005) 2004 AAAI Spring Symposium p.128-133
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... (More)

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.

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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
EXPLORING ATTITUDE AND AFFECT IN TEXT: THEORIES AND APPLICATIONS : Papers from the AAAI Spring Symposium - Papers from the AAAI Spring Symposium
pages
6 pages
publisher
AAAI Press
conference name
2004 AAAI Spring Symposium
conference location
Stanford, CA, United States
conference dates
2004-03-22 - 2004-03-24
external identifiers
  • scopus:32944475319
ISBN
978-1-57735-219-8
language
English
LU publication?
no
id
543f4930-fee3-4a75-912a-e0390dfba3e6
date added to LUP
2019-03-29 20:14:30
date last changed
2022-01-31 19:02:33
@inproceedings{543f4930-fee3-4a75-912a-e0390dfba3e6,
  abstract     = {{<p>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.</p>}},
  author       = {{Salvetti, Franco and Lewis, Stephen and Reichenbach, Christoph}},
  booktitle    = {{EXPLORING ATTITUDE AND AFFECT IN TEXT: THEORIES AND APPLICATIONS : Papers from the AAAI Spring Symposium}},
  isbn         = {{978-1-57735-219-8}},
  language     = {{eng}},
  month        = {{12}},
  pages        = {{128--133}},
  publisher    = {{AAAI Press}},
  title        = {{Impact of lexical filtering on overall opinion polarity identification}},
  year         = {{2005}},
}