Joint sentiment/topic model for sentiment analysis

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Authors

Chenghua Lin
Yulan HeORCiD: http://orcid.org/0000-0003-3948-5845

Research units

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.

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Details

Publication date	Nov 2009
Publication title	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
Original language	English
Event	18th ACM conference on Information and knowledge management - Hong Kong, China

Conference

Conference	18th ACM conference on Information and knowledge management
Abbreviated title	CIKM '09
Country	China
City	Hong Kong
Period	2/11/09 → 6/11/09

DOI

10.1145/1645953.1646003
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