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Credibility Analysis for Online Product Reviews

Credibility Analysis for Online Product Reviews

Min Chen, Anusha Prabakaran
Copyright: © 2018 |Volume: 9 |Issue: 3 |Pages: 18
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781522543800|DOI: 10.4018/IJMDEM.2018070103
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MLA

Chen, Min, and Anusha Prabakaran. "Credibility Analysis for Online Product Reviews." IJMDEM vol.9, no.3 2018: pp.37-54. http://doi.org/10.4018/IJMDEM.2018070103

APA

Chen, M. & Prabakaran, A. (2018). Credibility Analysis for Online Product Reviews. International Journal of Multimedia Data Engineering and Management (IJMDEM), 9(3), 37-54. http://doi.org/10.4018/IJMDEM.2018070103

Chicago

Chen, Min, and Anusha Prabakaran. "Credibility Analysis for Online Product Reviews," International Journal of Multimedia Data Engineering and Management (IJMDEM) 9, no.3: 37-54. http://doi.org/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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