Elements of information theory
Elements of information theory
Direction-based text interpretation as an information access refinement
Text-based intelligent systems
Making large-scale support vector machine learning practical
Advances in kernel methods
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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This paper presents a language modeling approach to the sentiment detection problem. It captures the subtle information in text processing to character the semantic orientation of documents as "thumb up" (positive) or "thumb down" (negative). To handle this problem, we propose an idea to estimate both the positive and negative language models from training collections. Tests are done through computing the Kullback-Leibler divergence between the language model estimated from test document and these two trained sentiment models. We assert the polarity of a test document by observing whether its language model is close to the trained "thumb up" model or the "thumb down" model. When compared with an outstanding classifier, i.e., SVMs on movie review corpus, language modeling approach showed its better performance.