Product review management software based on multiple classifiers. Issue 3 (1st June 2017)

• Record Type: Journal Article
• Authors: Catal, Cagatay; Guldan, Suat
• Is Part Of: IET software. Volume 11:Issue 3(2017)
• Page Start: 89
• Page End: 92
• Publication Date: 2017-06-01
• Subjects: consumer behaviour -- customer satisfaction -- pattern classification -- Internet -- decision making -- support vector machines -- optimisation -- learning (artificial intelligence) -- data mining -- software engineering; product review management software -- online shopping -- e-commerce -- consumer decision making -- fraudulent reviews -- multiple classifier system -- deceptive negative customer review identification -- hotel review dataset -- TripAdvisor -- majority voting combination rule -- libLinear -- libSVM -- sequential minimal optimisation -- random forest -- J48 -- support vector machines; Computer software -- Periodicals; Software engineering -- Periodicals; 005.1
• DOI: 10.1049/iet-sen.2016.0137
• Languages: English
• ISSNs: 1751-8806

Abstract

Abstract : In recent years, due to significant developments in online shopping and the widespread use of e‐commerce, competition among companies has increased considerably. As a result, product reviews have become a primary factor in consumers' decision making, which has given rise to a market for fraudulent reviews about real products and services. In this study, the authors propose a model using a multiple classifier system to identify deceptive negative customer reviews, which they validated with a dataset of hotel reviews from TripAdvisor. The proposed model used five classifiers by following the majority voting combination rule – namely, libLinear, libSVM, sequential minimal optimisation, random forest, and J48 – the first two of which represent different implementations of support vector machines. Ultimately, the model provided remarkable results that demonstrate improvement upon approaches reported in the literature.