Extracting Domain-Dependent Semantic Orientations of Latent Variables for Sentiment Classification

  • Yeha Lee
  • Jungi Kim
  • Jong-Hyeok Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)

Abstract

Sentiment analysis of weblogs is a challenging problem. Most previous work utilized semantic orientations of words or phrases to classify sentiments of weblogs. The problem with this approach is that semantic orientations of words or phrases are investigated without considering the domain of weblogs. Weblogs contain the author’s various opinions about multifaceted topics. Therefore, we have to treat a semantic orientation domain-dependently. In this paper, we present an unsupervised learning model based on aspect model to classify sentiments of weblogs. Our model utilizes domain-dependent semantic orientations of latent variables instead of words or phrases, and uses them to classify sentiments of weblogs. Experiments on several domains confirm that our model assigns domain-dependent semantic orientations to latent variables correctly, and classifies sentiments of weblogs effectively.

Keywords

sentiment classification sentiment analysis information extraction text mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yeha Lee
    • 1
  • Jungi Kim
    • 1
  • Jong-Hyeok Lee
    • 1
  1. 1.Division of Electrical and Computer EngineeringPohang University of Science and TechnologyPohangRepublic of Korea

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