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- 19 creator aoying-zhou.
- 19 creator jingwei-zhang.
- 19 creator xiaoling-wang.
- 19 creator yuming-lin.
- 19 type InProceedings.
- 19 label "An Information Theoretic Approach to Sentimental Polarity Classification".
- 19 sameAs 19.
- 19 abstract "Sentiment classification is a task of classifying documents according to their overall sentimental inclination. It is very important and popular in many web applications, such as analysis of credibility 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 weighting method) 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 sentimental labels based on information theory, and propose applying information theoretic approach on sentiment classification of documents. In this paper, the sentimental polarities of the terms in a document are quantified by mutual information. And then the terms are weighted in vector space based on their sentiment scores and contribution to the document. We perform extensive experiments with SVM on the sets of multiple products reviews, and the experimental results show our approach is more effective than the traditional ones.".
- 19 hasAuthorList authorList.
- 19 isPartOf proceedings.
- 19 keyword "feature presentation".
- 19 keyword "information theory".
- 19 keyword "mutual information".
- 19 keyword "sentiment classification".
- 19 title "An Information Theoretic Approach to Sentimental Polarity Classification".