Unmask inflated product reviews through Machine Learning

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI:10.1109/CIVEMSA52099.2021.9493576

M. Bangerter, G. Fenza, Mariacristina Gallo, Alessia Genovese, F. Nota, Claudio Stanzione, G. Zanfardino

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引用次数: 1

Abstract

Product reviews play a crucial role in online customer purchase decisions. In today’s marketplaces, such as Amazon, the quantity and interest of product reviews is growing, along with fierce competition between sellers. At the same time, the phenomenon of fake reviews is ever-growing. Many proposals capable of detecting fake reviews based on Machine Learning (ML) exist in literature. Nevertheless, bad practices implemented by the sellers make genuine reviews difficult to recognize. For instance, some Telegram Channels are known for giving products for free in exchange for 5-star reviews. This work focuses on the analysis of two review data streams of Amazon products. The first one is composed of the reviews corresponding to the products in the AmazonBasics category. The latter collects reviews of the products in the Telegram channels mentioned above. The analysis reveals a substantial dissimilarity between the two sources of reviews, that conducts to the construction of a ground truth dataset employed in the classification model training. The classification activity can assist during product rating interpretation, which could be invalidated by too many fake reviews. The experimental results reveal that 1&2-star reviews are good predictors of the review’s trustworthiness and the product itself.

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通过机器学习揭开夸大的产品评论

产品评论在网上顾客的购买决策中起着至关重要的作用。在今天的市场，比如亚马逊，产品评论的数量和兴趣都在增长，同时卖家之间的竞争也在激烈。与此同时，虚假评论的现象也越来越多。文献中存在许多基于机器学习(ML)的虚假评论检测方案。然而，卖家实施的不良做法使得真实的评论难以识别。例如，一些Telegram频道以免费提供产品以换取5星评价而闻名。本研究重点分析了亚马逊产品的两个评论数据流。第一个由与AmazonBasics类别中的产品相对应的评论组成。后者收集上面提到的Telegram频道中产品的评论。分析揭示了两种评论来源之间的巨大差异，这导致了分类模型训练中使用的地面真实数据集的构建。分类活动可以在产品评级解释期间提供帮助，因为太多的虚假评论可能会使其无效。实验结果表明，1星和2星评论是评论可信度和产品本身的良好预测指标。

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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2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

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