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International Conference on Web-Age Information Management

WAIM 2016: Web-Age Information Management pp 95–106Cite as

A Novel Chinese Text Mining Method for E-Commerce Review Spam Detection

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9658))

Abstract

Review spam is increasingly rampant in China, which seriously hampers the development of the vigorous e-commerce market. In this paper, we propose a novel Chinese text mining method to detect review spam automatically and efficiently. We correctly extract keywords in complicated review text and conduct fine-grained analysis to recognize the semantic orientation. We study the spammers’ behavior patterns and come up with four effective features to describe untruthful comments. We train classifier to classify reviews into spam or non-spam. Experiments are conducted to demonstrate the excellent performance of our algorithm.

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Author information

Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518000, China

    Xiu Li & Xinwei Yan

Authors
  1. Xiu Li
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  2. Xinwei Yan
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Corresponding author

Correspondence to Xiu Li .

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University, Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Li, X., Yan, X. (2016). A Novel Chinese Text Mining Method for E-Commerce Review Spam Detection. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-39937-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39936-2

  • Online ISBN: 978-3-319-39937-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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