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New Technology of Library and Information Service  2013, Vol. 29 Issue (1): 63-68    DOI: 10.11925/infotech.1003-3513.2013.01.10
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Research on Review Spam Recognition
Li Xiao, Ding Shengchun
Department of Information and Management, Nanjing University of Science & Technology, Nanjing 210094, China
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Abstract  This paper analyses review spam from the perspective of the usefulness of information, selects digital camera reviews as the research object and builds the data set, then from the three aspects of review, reviewer and product chooses 11 features, uses 4 different kernel functions in SVM model to identify review spam of products, optimizes the parameters C and γ of RBF that has a better identification, which improves accuracy rate of the identification effect of review spam to 78.16% and recall rate to 72.18%. By comparing the selected 4 different combinations of features, the authors find the combination of review, reviewer and product is the best. Finally, it proves that SVM is significantly better than other algorithms compared to the Logistic Regression.
Key wordsSVM      Review spam      Feature selection      Kernel function      Product review     
Received: 08 January 2013      Published: 29 March 2013



Cite this article:

Li Xiao, Ding Shengchun. Research on Review Spam Recognition. New Technology of Library and Information Service, 2013, 29(1): 63-68.

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