Credibility Analysis for Online Product Reviews

Credibility Analysis for Online Product Reviews

Min Chen, Anusha Prabakaran
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJMDEM.2018070103
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Abstract

With the prevalence of e-commerce, online product reviews are increasingly considered crowd-sourced consumer opinions that significantly influence customer purchasing decisions and product rankings. It is therefore important to ensure the truthfulness of reviews by detecting and filtering out fake/spam reviews. This article presents an effective framework to analyze review credibility for spam detection and opinion mining. It incorporates three methods: duplicated review detection, anomaly detection, and incentivized review detection, that complement each other to produce statistical credibility scores indicating review credibility. A practical end-to-end system is designed and developed accordingly, and is equipped with high-level data visualization for easy interpretation and summarization of the analysis results. Experiments on an Amazon review dataset demonstrate its efficiency, scalability and accuracy. This system could help e-commerce and consumers identify fake reviews, refine product rankings, and constrain vendors and spammers from engaging in dishonest practices.
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Fake reviews are written by spammers in order to manipulate the perceived value of products sold online. Detection of review spam is important as manual assessment of reviews and distinguishing real opinions from fake reviews is nearly impossible (Jindal & Liu, 2008).

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