基于评论产品属性情感倾向评估的虚假评论识别研究

Research on Product Review Attribute-Based of Emotion Evaluate Review Spam Detection

【目的】提出一种基于评论产品属性情感倾向评估模型(Review Attribute of Product-Based Emotion Evaluate,RAPBEE模型),用于在线商品虚假评论的识别。【方法】针对在线商品虚假评论采用评论产品属性情感倾向离群度量方法,结合已有评论效用研究对评论结果进行综合排序,从而得出评论的可信度序列。【结果】基于R语言实现,在模型试验集上,通过RAPBEE模型识别处理后的评论序列和当前商品真实情况的符合度为86.2%,实验结果表明RAPBEE模型有较强的实际应用能力与适应度。【局限】需要依赖于已有属性词典的建模方式,在大规模的数据运行效率上有待改进。【结论】提供一种新的针对中文商品虚假评论识别处理方法,具有较强的扩展能力。

[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 expandability in reality. detection of online products, and this method has a strong