Paper Info
Title
An information theoretic approach to sentiment polarity classification
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.
Year
DOI
Venue
2012
10.1145/2184305.2184313
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Keywords
DocType
Citations 
sentiment score,sentiment polarity,mutual information,sentiment polarity classification,classification accuracy,supervised sentiment classification,information theory,information theoretic approach,classifying document,overall sentiment inclination,sentiment classification,vector space,weight function,vector space model,recommender system
Conference
18
PageRank 
References 
Authors
0.67
20
4
Name
Order
Citations
PageRank
Yuming Lin1374.76
Jingwei Zhang2272.02
Xiaoling Wang346972.53
Aoying Zhou42632238.85