An ensemble classification model for fake feedback detection using proposed labeled CloudArmor dataset

Abstract

With an increase in the use of Cloud Services, Cloud Customers (CCs) have started browsing reviews about various Cloud Service Providers (CSPs) online provided by numerous CCs to gather their sentiments. This pattern has proved to be fruitful, but it can become ineffective if fake reviews are posted. This article aims at introducing an ensemble model to classify reviews as fake or genuine on the proposed labeled CloudArmour dataset. Labeling has been achieved through these aspects: Number of Reviews provided by a CC to single CSP; Number of CSPs reviewed by a CC; & Frequency of feedbacks published. Additionally, we have compared the performance of the proposed ensemble model with these supervised machine learning classification techniques: KNN; Logistic_Regression; and SVM_rbf on the basis of the following parameters: accuracy; precision, recall; and F1-Score. Ensemble model has yielded highest values as: 97.51%; 98.19%; 93.65%; & 95.86% respectively. Thus, we conclude that the ensemble model has outperformed other models.