Fake Reviews Detection Using Supervised Machine Learning
Keywords:
Machine learning, fake, reviews, Logistic RegressionAbstract
With the ongoing evolution of E-commerce platforms, online evaluations are increasingly seen as a critical aspect in establishing and maintaining a positive reputation. Furthermore, they play an important role in end-user decision making. A positive evaluation for a target object typically draws more customers and results in a significant rise in sales. Deceptive or phoney evaluations are now intentionally generated in order to build a virtual reputation and attract potential clients. Identifying bogus reviews is thus an active and ongoing research topic. Detecting phoney reviews is dependent not only on the primary elements of the reviews, but also on the reviewers' behaviour. This research suggests using machine learning to detect bogus reviews. In addition to the review features extraction approach, this research utilises different features engineering techniques to extract distinct reviewer behaviours. The study examines the performance of machine learning classifiers KNN, Naive Bayes (NB), and Logistic Regression using a genuine Yelp dataset of restaurant reviews. In terms of accuracy, the results show that Logistic Regression surpasses all other classifiers. The results reveal that the algorithm is more capable of distinguishing between genuine and false reviews.
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