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Joint Naïve Bayes and LDA for Unsupervised Sentiment Analysis

  • Yong Zhang
  • Dong-Hong Ji
  • Ying Su
  • Hongmiao Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7818)

Abstract

In this paper we proposed a hierarchical generative model based on Naïve Bayes and LDA for unsupervised sentiment analysis at sentence level and document level of granularity simultaneously. In particular, our model called NB-LDA assumes that each sentence instead of word has a latent sentiment label, and then the sentiment label generates a series of features for the sentence independently in the Naïve Bayes manner. The idea of NB assumption at sentence level makes it possible that we can use advanced NLP technologies such as dependency parsing to improve the performance for unsupervised sentiment analysis. Experiment results show that the proposed NB-LDA can obtain significantly improved results for sentiment analysis comparing to other approaches.

Keywords

Sentiment Analysis Latent Dirichlet Allocation Naïve Bayes Opinion Mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yong Zhang
    • 1
    • 2
  • Dong-Hong Ji
    • 1
  • Ying Su
    • 3
  • Hongmiao Wu
    • 4
  1. 1.Computer SchoolWuhan UniversityWuhanP.R. China
  2. 2.Department of Computer ScienceHuazhong Normal UniversityWuhanP.R. China
  3. 3.Department of Computer Science, Wuchang BranchHuazhong University of Science and TechnologyWuhanP.R. China
  4. 4.School of Foreign Languages and LiteratureWuhan UniversityWuhanP.R. China

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