Sentiment analysis of chinese documents: From sentence to document level

Changli Zhang, Daniel Zeng, Jiexun Li, Fei Yue Wang, Wanli Zuo

Research output: Contribution to journalArticlepeer-review

149 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2474-2487
Number of pages14
JournalJournal of the American Society for Information Science and Technology
Volume60
Issue number12
DOIs
StatePublished - Dec 2009

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence

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