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Learning opinions in user-generated web content

Published online by Cambridge University Press:  11 March 2011

M. SOKOLOVA  and
G. LAPALME
M. SOKOLOVA
Affiliation:
Department of Pediatrics, Faculty of Medicine, Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, 401 Smyth Rd., Ottawa, Ontario, Canada, K1H 8L1 email: sokolova@uottawa.ca
G. LAPALME
Affiliation:
Département d'informatique et de recherche opérationnelle, Université de Montréal, C.P. 6128, Succ Centre-Ville, Montréal, Quebec, Canada, H3C 3J7 email: lapalme@iro.umontreal.ca
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Abstract

The user-generated Web content has been intensively analyzed in Information Extraction and Natural Language Processing research. Web-posted reviews of consumer goods are studied to find customer opinions about the products. We hypothesize that nonemotionally charged descriptions can be applied to predict those opinions. The descriptions may include indicators of product size (tall), commonplace (some), frequency of happening (often), and reviewer certainty (maybe). We first construct patterns of how the descriptions are used in consumer-written texts and then represent individual reviews through these patterns. We propose a semantic hierarchy that organizes individual words into opinion types. We run machine learning algorithms on five data sets of user-written product reviews: four are used in classification experiments, another one for regression and classification. The obtained results support the use of non-emotional descriptions in opinion learning.

Type
Articles
Information
Natural Language Engineering , Volume 17 , Issue 4 , October 2011 , pp. 541 - 567
Copyright
Copyright © Cambridge University Press 2011

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