NDLI: An information theoretic approach to sentiment polarity classification

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An information theoretic approach to sentiment polarity classification

Content Provider	ACM Digital Library
Author	Zhang, Jingwei Lin, Yuming Wang, Xiaoling Zhou, Aoying
Abstract	Sentiment classification is a task of classifying documents according to their overall sentiment inclination. It is very important and popular in many web applications, such as credibility analysis of news sites on the Web, recommendation system and mining online discussion. Vector space model is widely applied on modeling documents in supervised sentiment classification, in which the feature presentation (including features type and weight function) is crucial for classification accuracy. The traditional feature presentation methods of text categorization do not perform well in sentiment classification, because the expressing manners of sentiment are more subtle. We analyze the relationships of terms with sentiment labels based on information theory, and propose a method by applying information theoretic approach on sentiment classification of documents. In this paper, we adopt mutual information on quantifying the sentiment polarities of terms in a document firstly. Then the terms are weighted in vector space based on both sentiment scores and contribution to the document. We perform extensive experiments with SVM on the sets of multiple product reviews, and the experimental results show our approach is more effective than the traditional ones.
Starting Page	35
Ending Page	40
Page Count	6
File Format	PDF
ISBN	9781450312370
DOI	10.1145/2184305.2184313
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature presentation Sentiment classification Mutual information Information theory
Content Type	Text
Resource Type	Article

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