Fake Review Detection Using Hybrid Ensemble Learning

Abstract

Opinion spam on online restaurant review sites are a major problem as the reviews influence the users’ choice to visit or not to a restaurant. In this paper, we address the problem of detecting genuine and fake reviews in restaurant online reviews. We propose a fake review detection technique comprising data preprocessing, detection and ensemble learning that learns the reviews and their features to filter out the fake reviews. Initially, we preprocess to obtain the refined reviews and employ two independent classifiers using deep machine learning and feature-based machine learning techniques for detection. These classifiers tackle the problem in two aspects, i.e., the deep machine learning model learns the word distributions and the feature-based machine learning model extracts the relevant features from the reviews. Finally, a hybrid ensemble model from the two classifiers are built to detect the genuine and fake reviews. The experimental analysis of the proposed approach on Yelp datasets outperforms the existing state-of-the-art methods.