Every Term Has Sentiment: Learning from Emoticon Evidences for Chinese Microblog Sentiment Analysis

  • Fei Jiang
  • Anqi Cui
  • Yiqun Liu
  • Min Zhang
  • Shaoping Ma
Part of the Communications in Computer and Information Science book series (CCIS, volume 400)

Abstract

Chinese microblog is a popular Internet social medium where users express their sentiments and opinions. But sentiment analysis on Chinese microblogs is difficult: The lack of labeling on the sentiment polarities restricts many supervised algorithms; out-of-vocabulary words and emoticons enlarge the sentiment expressions, which are beyond traditional sentiment lexicons. In this paper, emoticons in Chinese microblog messages are used as annotations to automatically label noisy corpora and construct sentiment lexicons. Features including microblog-specific and sentiment-related ones are introduced for sentiment classification. These sentiment signals are useful for Chinese microblog sentiment analysis. Evaluations on a balanced dataset are conducted, showing an accuracy of 63.9% in a three-class sentiment classification of positive, negative and neutral. The features mined from the Chinese microblogs also increase the performances.

Keywords

Microblog Sentiment Analysis Sentiment Lexicon Construction Support Vector Machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fei Jiang
    • 1
    • 2
    • 3
  • Anqi Cui
    • 1
    • 2
    • 3
  • Yiqun Liu
    • 1
    • 2
    • 3
  • Min Zhang
    • 1
    • 2
    • 3
  • Shaoping Ma
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Intelligent Technology and SystemsTsinghua UniversityBeijingChina
  2. 2.Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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