Product review management software based on multiple classifiers

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Çatal, Çağatay
Güldan, Suat
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Inst Engineering Technology-Iet, Michael Faraday House Six Hills Way Stevenage, Hertford Sg1 2Ay, England
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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.
Anahtar kelimeler
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