Detecting Fake Reviews Utilizing Semantic and Emotion Model

Yuejun Li, Xiao Feng, Shuwu Zhang
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引用次数: 37

Abstract

As people are spending more time to shop and view reviews on line, some reviewer write fake reviews to earn credit and to promote (demote) the sales of product and stores. Detecting fake reviews and spammers becomes more important when the spamming behavior is becoming damaging. This paper proposes three types of new features which include review density, semantic and emotion and gives the model and algorithm to construct each feature. Experiments show that the proposed model, algorithm and features are efficient in fake review detection task than traditional method based on content, reviewer info and behavior.
基于语义和情感模型的虚假评论检测
随着人们花更多的时间在网上购物和查看评论,一些评论者写虚假评论来获得信用,并促进(降低)产品和商店的销售。当垃圾邮件行为变得具有破坏性时,检测虚假评论和垃圾邮件发送者变得更加重要。本文提出了评论密度、语义和情感三种新特征,并给出了构建每种特征的模型和算法。实验表明,本文提出的模型、算法和特征比传统的基于内容、审稿人信息和行为的假评论检测方法更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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