EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection

Giuseppina Andresini , Andrea Iovine , Roberto Gasbarro , Marco Lomolino , Marco de Gemmis , Annalisa Appice
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引用次数: 4

Abstract

Nowadays, online reviews are the main source to customer opinions. They are especially important in the realm of e-commerce, where reviews regarding products and services influence the purchase decisions of customers, as well as the reputation of the commerce websites. Unfortunately, not all the online reviews are truthful and trustworthy. Therefore, it is crucial to develop machine learning techniques to detect review spam. This study describes EUPHORIA — a novel classification approach to distinguish spam from truthful reviews. This approach couples multi-view learning to deep learning, in order to gain accuracy by accounting for the variety of information possibly associated with both the reviews’ content and the reviewers’ behavior. Experiments carried out on two real review datasets from Yelp.com – Hotel and Restaurant – show that the use of multi-view learning can improve the performance of a deep learning classifier trained for review spam detection. In particular, the proposed approach achieves AUC-ROC equal to 0.813 and 0.708 in Hotel and Restaurant, respectively.

EUPHORIA:一种结合评论垃圾邮件检测内容和行为特征的神经多视图方法
如今,网上评论是顾客意见的主要来源。它们在电子商务领域尤其重要,在电子商务领域,关于产品和服务的评论会影响客户的购买决策,以及商务网站的声誉。不幸的是,并非所有的在线评论都是真实可信的。因此,开发机器学习技术来检测评论垃圾邮件至关重要。本研究描述了EUPHORIA——一种区分垃圾邮件和真实评论的新分类方法。这种方法将多视图学习与深度学习相结合,以便通过考虑可能与审稿人的内容和审稿人的行为相关的各种信息来获得准确性。在Yelp.com的两个真实评论数据集(酒店和餐厅)上进行的实验表明,使用多视图学习可以提高深度学习分类器的性能,用于评论垃圾邮件检测。特别是,本文方法在酒店和餐厅的AUC-ROC分别为0.813和0.708。
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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