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| Research on Product Review Attribute-Based of Emotion Evaluate Review Spam Detection |
| Chen Yanfang1,2, Li Zhiyu1,2,3 |
1. School of Information Management, Central China Normal University, Wuhan 430079, China;
2. E-Commerce Research Center of Hubei Province, Central China Normal University, Wuhan 430079, China;
3. School of Information, Renmin University of China, Beijing 100872, China |
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Abstract [Objective] A model of Review Attribute of Product-Based Emotion Evaluate(RAPBEE) Model is proposed to detect fake reviews of online products. [Methods] Combined with the known research on the reviews effectiveness evaluation, the measuring method of review attribute of product-based emotion outlier detection is used to comprehensive sort the reliability of the reviews, so as to detect the fake reviews. [Results] The test data set is based on the R language to run the model, the results show that after calculated by the RAPBEE model the review sequencing has achieved 86.2% of agreement compared with the real situation which indicates that the RAPBEE model has a strong practical ability and fitness. [Limitations] The model stability depends on the modeling way of the attribute dictionary and the method also can be improved when dealing with large amounts of data set(Big Data). [Conclusions] The paper proposes a new method to deal with the Chinese fake reviews detection of online products, and this method has a strong expandability in reality.
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Received: 24 March 2014
Published: 20 October 2014
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