Boosting Accuracy of Fake Review Prediction Using Synthetic Minority Oversampling Technique

Abstract

In recent times prior to making a purchase, the vast majority read reviews about that product, and their decision is largely driven by the reviews. Deceitful online sellers often gather fake or spam reviews for their products or services, thereby reducing the effectiveness of online reviews. The review data is often imbalanced such that the fake reviews greatly outnumber the genuine reviews. An imbalance leads to a bias, as the model tends to mostly predict the majority class. To attain a high-quality classification outcome, the issue of imbalanced data should be resolved before applying the classification algorithms. This paper studies the performance of supervised machine learning classifiers pertaining to fake review detection. The approach put forward in this paper aims to improve the prediction accuracy of popular supervised learning classifiers Random - Forest, LightGBM, XGBoost, Naive Bayes, and Decision Tree on an imbalanced review dataset For boosting the accuracy of these classifiers, the Synthetic Minority Oversampling Technique is used for addressing the class imbalance problem. The performance of the classifiers has been studied by changing the oversampling parameters. The application of SMOTE showed a significant improvement in the classifier's prediction accuracy.