Product review management software based on multiple classifiers - Çatal, Çağatay; Güldan, Suat (Inst Engineering Technology-Iet, Michael Faraday House Six Hills Way Stevenage, Hertford Sg1 2Ay, England, 2017)
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.
Keywords: 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