Joint sentiment/topic model for sentiment analysis

Chenghua Lin, Yulan He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

563 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 18th ACM conference on Information and knowledge management
Place of PublicationNew York
PublisherACM Press
Pages375-384
Number of pages10
ISBN (Print)978-1-60558-512-3
DOIs
Publication statusPublished - 2 Nov 2009

Keywords

  • sentiment analysis
  • opinion mining
  • latent dirichlet allocation
  • joint sentiment/topic model

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  • Cite this

    Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375-384). ACM Press. https://doi.org/10.1145/1645953.1646003