Noise reduction in a statistical approach to text categorization
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
PHOAKS: a system for sharing recommendations
Communications of the ACM
Virtual reviewers for collaborative exploration of movie reviews
Proceedings of the 5th international conference on Intelligent user interfaces
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Using a sentiment map for visualizing credibility of news sites on the web
Proceedings of the 2nd ACM workshop on Information credibility on the web
Sentiment vector space model for lyric-based song sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
A study of information retrieval weighting schemes for sentiment analysis
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Sentiment classification using word sub-sequences and dependency sub-trees
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Assembling the optimal sentiment classifiers
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Revised mutual information approach for german text sentiment classification
Proceedings of the 22nd international conference on World Wide Web companion
Pessimists and optimists: Improving collaborative filtering through sentiment analysis
Expert Systems with Applications: An International Journal
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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.