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| Feature Extraction Method for Detecting Spam in Electronic Commerce |
| You Guirong1,2, Wu Wei3, Qian Yuntao2 |
1. Department of Information Management Engineering, Fujian Commercial College, Fuzhou 350012, China;
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
3. Zhejiang Province Key Laboratory of Network System and Information Security, Hangzhou 310006, China |
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Abstract [Objective] A feature extraction method is proposed aiming to detect spams and improve recognition rate from regular product reviews in electronic commerce. [Methods] Based on the idea of quantitative evaluation, features are extracted comprehensively in terms of reviews' intrinsic characters such as the number of evaluation sentence, sentiment tendency, topic word and text structure. The number of evaluation sentence is the key feature to distinguish spams from regular product reviews using Part-Of-Speech (POS) path matching templates, and a custom dictionary is imported to improve recognition rate of detecting evaluation sentence. [Results] Experiment results show that the spam recognition precision can reach 97.96% and F-measure reach 88.48%. [Limitations] This method is mainly used to identify Chinese review spams, is not suitable for the English product reviews. [Conclusions] Review spams can be effectively and accurately detected by the proposed features. Furthermore, these features can also be applied to evaluate and rank the regular product reviews, and other related applications.
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Received: 01 April 2014
Published: 28 November 2014
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