Joint sentiment/topic model for sentiment analysis

Lin, Chenghua and He, Yulan (2009). Joint sentiment/topic model for sentiment analysis. In: Proceeding of the 18th ACM conference on Information and knowledge management pp. 375–384.

DOI: https://doi.org/10.1145/1645953.1646003

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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About

• Item ORO ID
• 23786
• Item Type
• Conference or Workshop Item
• Keywords
• sentiment analysis; opinion mining; Latent Dirichlet Allocation; joint sentiment/topic model
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Research Group
• Centre for Research in Computing (CRC)
• Copyright Holders
• © 2009 ACM
• Depositing User
• Kay Dave