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Thumbs up?: sentiment classification using machine learning techniques
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ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Topic sentiment mixture: modeling facets and opinions in weblogs
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ACM Transactions on Information Systems (TOIS)
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Multi-domain sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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Learning sentiment classification model from labeled features
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Self-training from labeled features for sentiment analysis
Information Processing and Management: an International Journal
Latent sentiment model for weakly-supervised cross-lingual sentiment classification
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
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AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Computer Speech and Language
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This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentiment-topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection.