access icon free Product review management software based on multiple classifiers

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.

Inspec keywords: optimisation; support vector machines; software engineering; decision making; data mining; pattern classification; consumer behaviour; Internet; customer satisfaction; learning (artificial intelligence)

Other keywords: multiple classifier system; support vector machines; sequential minimal optimisation; consumer decision making; online shopping; random forest; fraudulent reviews; J48; deceptive negative customer review identification; hotel review dataset; libSVM; majority voting combination rule; product review management software; e-commerce; libLinear; TripAdvisor

Subjects: Information networks; Marketing computing; Data handling techniques; Optimisation techniques; Knowledge engineering techniques; Game theory

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