Fake Review Detection and Removal: A Comparative Analysis using ML and DL Models

Reviews are increasingly used for purchase decisions by the customers, they are important for e-commerce and social networking sites. However, not every review is necessarily authentic. Researchers have put out a variety of machine learning techniques in the past to identify false product reviews. Finding the proper machine learning algorithm to spot fake reviews for a particular type of data is crucial, though. Consequently, in this research, algorithms such as SVC (Support Vector Classifier), Decision Tree Classifier, Logistic Regression, Random Forests Classifier, Multinomial Naive Bayes and k-Nearest Neighbors are compared on different kinds of datasets like Amazon dataset, Yelp Dataset and TripAdvisor Hotel Reviews Dataset. Accuracy, Recall, Precision, and F1-Measure evaluation findings are the basis for the comparison. The findings of this study indicate that, Support Vector classifier comes out to be the best performing algorithm for detecting fake reviews when compared with the other five techniques. While the k-Nearest Neighbors algorithm has the worst performance