Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks

Online reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated (genuine) as well as computer-generated (fake) reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this work, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem.