使用半监督学习和监督学习检测虚假在线评论

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI:10.1109/ECACE.2019.8679186

Rakibul Hassan, M. Islam

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引用次数: 24

摘要

在线评论对今天的商业和商业有很大的影响。购买在线产品的决策大多依赖于用户给出的评论。因此，投机取巧的个人或团体试图为了自己的利益操纵产品评论。本文介绍了一些半监督和监督文本挖掘模型来检测虚假在线评论，并比较了两种技术在包含酒店评论的数据集上的效率。

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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Detection of fake online reviews using semi-supervised and supervised learning

Online reviews have great impact on today's business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.

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来源期刊

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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