Battering Review Spam Through Ensemble Learning in Imbalanced Datasets

Nowadays, people's buying or availing services decisions are subject to online available reviews/opinions. The authenticity of these reviews/opinions is dubious, as there exist many fake reviews posted to attain monetary benefits by promoting their own or demoting the competitor's products or services known as review spam. Although the number of spam is relatively less than that of normal reviews in real-life, this class imbalance is a critical concern in review spam detection. The performance degrades when the classifier skew towards the majority class. Moreover, efficient feature selection is essentially needed for this issue. The purpose of this study is to develop a framework based on different effective feature selection along with data balancing techniques. Validation results show that our proposed framework commendably copes up with the review spam issue and a higher precision on the real-life dataset. Further, we tested the sensitivity of our proposed framework using both parametric and non-parametric tests and found it significant.