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International Conference on Machine Learning and Cybernetics

ICMLC 2014: Machine Learning and Cybernetics pp 13–23Cite as

Sentiment Classification of Chinese Reviews in Different Domain: A Comparative Study

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

Abstract

With the rapid development of micro-blog, blog and other types of social media, users’ reviews on the social media increase dramatically. Users’ reviews mining plays an important role in the application of product information or public opinion monitoring. Sentiment classification of users’ reviews is one of key issues in the review mining. Comparative study on sentiment classification results of reviews in different domains and the adaptability of sentiment classification methods is an interesting research topic. This paper classifies users’ reviews in three different domains based on Support Vector Machine with six kinds of feature weighting methods. Experiment results in three domains indicate that different domains have their own characteristics and the selection of feature weighting methods should consider the domain characteristics.

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Author information

Authors and Affiliations

  1. Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094, China

    Qingqing Zhou & Chengzhi Zhang

Authors
  1. Qingqing Zhou
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  2. Chengzhi Zhang
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Corresponding author

Correspondence to Chengzhi Zhang .

Editor information

Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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© 2014 Springer-Verlag Berlin Heidelberg

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Zhou, Q., Zhang, C. (2014). Sentiment Classification of Chinese Reviews in Different Domain: A Comparative Study. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_2

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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