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A risk detection system of e-commerce: researches based on soft information extracted by affective computing web texts

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  • Anzhong Huang

    (Jiangsu Normal University)

Abstract

The product comments in e-commerce circumstance has a strong guidance role on customers, therefore the merchants will usually falsify bogus comments to defraud customers. In order to monitor such behaviors of merchants, we transform the comments of the same product from different merchants to eigenvector by the way of affective computing, then compare it with the vector derived from the comments on the third-party professional assessment and test websites as well as on the microblog, and identify those vectors with big deviation degree, that means the product of those merchants are possibly in fraudulence. Experiment proves that the system can recognize fraudulent merchants.

Suggested Citation

  • Anzhong Huang, 2018. "A risk detection system of e-commerce: researches based on soft information extracted by affective computing web texts," Electronic Commerce Research, Springer, vol. 18(1), pages 143-157, March.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:1:d:10.1007_s10660-017-9262-y
    DOI: 10.1007/s10660-017-9262-y
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    References listed on IDEAS

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    1. Hitoshi Yamamoto & Kazunari Ishida & Toshizumi Ohta, 2004. "Modeling Reputation Management System on Online C2C Market," Computational and Mathematical Organization Theory, Springer, vol. 10(2), pages 165-178, July.
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    Cited by:

    1. Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.

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