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Partially Supervised Phrase-Level Sentiment Classification

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

Abstract

This paper presents a new partially supervised approach to phrase-level sentiment analysis that first automatically constructs a polarity-tagged corpus and then learns sequential sentiment tag from the corpus. This approach uses only sentiment sentences which are readily available on the Internet and does not use a polarity-tagged corpus which is hard to construct manually. With this approach, the system is able to automatically classify phrase-level sentiment. The result shows that a system can learn sentiment expressions without a polarity-tagged corpus.

Keywords

sentiment classification sentiment analysis information extraction text mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sang-Hyob Nam
    • 1
  • Seung-Hoon Na
    • 1
  • Jungi Kim
    • 1
  • Yeha Lee
    • 1
  • Jong-Hyeok Lee
    • 1
  1. 1.Division of Electrical and Computer EngineeringPohang University of Science and TechnologyPohangRepublic of Korea

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