Abstract
Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.
Original language | English |
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Title of host publication | CIKM '09 Proceedings of the 18th ACM conference on Information and knowledge management |
Place of Publication | New York (US) |
Publisher | ACM |
Pages | 375-384 |
Number of pages | 10 |
ISBN (Print) | 978-1-60558-512-3 |
DOIs | |
Publication status | Published - Nov 2009 |
Event | 18th ACM conference on Information and knowledge management - Hong Kong, China Duration: 2 Nov 2009 → 6 Nov 2009 |
Conference
Conference | 18th ACM conference on Information and knowledge management |
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Abbreviated title | CIKM '09 |
Country/Territory | China |
City | Hong Kong |
Period | 2/11/09 → 6/11/09 |