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Joint sentiment/topic model for sentiment analysis

Lin, Chenghua and He, Yulan (2009). Joint sentiment/topic model for sentiment analysis. In: The 18th ACM Conference on Information and Knowledge Management (CIKM), 11 Nov 2010, Hong Kong, China, pp. 375–384.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1145/1645953.1646003
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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.

Item Type: Conference Item
Copyright Holders: 2009 ACM
Keywords: sentiment analysis; opinion mining; Latent Dirichlet Allocation; joint sentiment/topic model
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23786
Depositing User: Kay Dave
Date Deposited: 14 Oct 2010 15:55
Last Modified: 23 Apr 2013 10:08
URI: http://oro.open.ac.uk/id/eprint/23786
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