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Learning User Credibility on Aspects from Review Texts

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Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9659))

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Abstract

Spammer detection has been popularly studied these years which aims at filtering unfair or incredible customers. Most users have different backgrounds or preferences so that they make distinct reviews/ratings, however they can not be treated as spammers. To date, the existing previous spammer detection technology has limited usability. In this paper, we propose a method to calculate user credibility on multi-dimensions by considering users difference related to their personalities e.g. background and preference. Firstly, we propose to evaluate customer credibilities on aspects with the consideration of different concerns given by different customers. A boot-strapping algorithm is applied to detect the intrinsic aspects of review text and the aspect ratings are assigned by mining semantic polarity. Then, an iteration algorithm is designed for estimating credibilities by considering the consistency between individual ratings and overall ratings on aspects. Finally, experiments on the real dataset demonstrate that our method outperforms baseline systems.

Corresponding author is Rong Zhang

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Notes

  1. 1.

    Dianping: http://www.dianping.com.

  2. 2.

    http://www.ansj.org/.

  3. 3.

    http://sifaka.cs.uiuc.edu/~wang296/Codes/LARA.zip.

  4. 4.

    http://www.datatang.com/data/44317/.

  5. 5.

    http://pan.baidu.com/s/1sjoqp1z.

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Acknowledgment

This work is partially supported by National Science Foundation of China under grant (No. 61103039 and NO. 61402180), and National Science Foundation of Shanghai (No. 14ZR1412600).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, Institute for Data Science and Engineering, East China Normal University, Shanghai, China

    Yifan Gao, Yuming Li, Yanhong Pan, Jiali Mao & Rong Zhang

Authors
  1. Yifan Gao
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  2. Yuming Li
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  3. Yanhong Pan
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  4. Jiali Mao
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  5. Rong Zhang
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Corresponding author

Correspondence to Rong Zhang .

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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Gao, Y., Li, Y., Pan, Y., Mao, J., Zhang, R. (2016). Learning User Credibility on Aspects from Review Texts. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-39958-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39957-7

  • Online ISBN: 978-3-319-39958-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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