Joint sentiment/topic model for sentiment analysis

10 years 11 months ago
Joint sentiment/topic model for sentiment analysis
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. Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural Language Processing—Text analysis General Terms Algorithms, Experimentation Keywords Sentiment...
Chenghua Lin, Yulan He
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Chenghua Lin, Yulan He
Comments (0)