IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v60y2009i12p2474-2487.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.21206
    Download Restriction: no

    File URL: https://libkey.io/10.1002/asi.21206?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jamist:v:60:y:2009:i:12:p:2474-2487. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.