Sentiment analysis of Chinese documents: From sentence to document level

  • Authors:

  • Changli Zhang;Daniel Zeng;Jiexun Li;Fei-Yue Wang;Wanli Zuo

  • Affiliations:

  • College of Computer Science and Technology, Jilin University, China, and Artillery Command College of Shenyang, China;MIS Department, University of Arizona and the Institute of Automation, Chinese Academy of Sciences, Beijing, China;College of Information Science and Technology, Drexel University, Philadelphia, PA;Institute of Automation, Chinese Academy of Sciences, Beijing, China;College of Computer Science and Technology, Jilin University, China

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2009

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Abstract

User-generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule-based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning-based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches. © 2009 Wiley Periodicals, Inc.