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Sentiment analysis of Chinese documents: From sentence to document level

Author

Listed:
  • Changli Zhang
  • Daniel Zeng
  • Jiexun Li
  • Fei‐Yue Wang
  • Wanli Zuo

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.

Suggested Citation

  • Changli Zhang & Daniel Zeng & Jiexun Li & Fei‐Yue Wang & Wanli Zuo, 2009. "Sentiment analysis of Chinese documents: From sentence to document level," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(12), pages 2474-2487, December.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:12:p:2474-2487
    DOI: 10.1002/asi.21206
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    Cited by:

    1. Li, Wanqi, 2024. "“Amusing ourselves to death”: Mechanisms in cyberbullying prompted by rumors and denigration amidst the COVID-19 pandemic in China," Technology in Society, Elsevier, vol. 76(C).
    2. Hui Zhang & Huguang Rao & Junzheng Feng, 2018. "Product innovation based on online review data mining: a case study of Huawei phones," Electronic Commerce Research, Springer, vol. 18(1), pages 3-22, March.
    3. Xiao, Yan & Li, Congdong & Thürer, Matthias & Liu, Yide & Qu, Ting, 2022. "User preference mining based on fine-grained sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    4. Mohammed Rushdi‐Saleh & M. Teresa Martín‐Valdivia & L. Alfonso Ureña‐López & José M. Perea‐Ortega, 2011. "OCA: Opinion corpus for Arabic," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 2045-2054, October.
    5. Ghasem Javadi & Mohammad Taleai, 2020. "Integration of User Generated Geo-contents and Official Data to Assess Quality of Life in Intra-national Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 205-235, November.
    6. Gang Wang & Daqing Zheng & Shanlin Yang & Jian Ma, 2018. "FCE-SVM: a new cluster based ensemble method for opinion mining from social media," Information Systems and e-Business Management, Springer, vol. 16(4), pages 721-742, November.

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