Impact of lexical filtering on overall opinion polarity identification
(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.
(Less)
- author
- Salvetti, Franco ; Lewis, Stephen and Reichenbach, Christoph LU
- publishing date
- 2005-12-01
- 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}}, }