Authors - Jayalakshmi D, N. Priya Abstract - Online product reviews play a key role in the success or failure of an e-commerce business. Often, online reviews from previous customers provide buyers with detailed advice about the product and help them decide before purchasing a product or service. However, some e-commerce products can be promoted or damaged by fraudsters who post fake reviews. Synthetic Reviews (SRs) have the capacity to deceive consumers, influence purchasing decisions, and lead to losses. Thus, SRs pose a significant risk to e-commerce companies and content creators, undermining consumer loyalty and brand reputation. Specifically, the development of AI-generated fake reviews has made them harder to detect, as they are very similar to human-written texts. This review paper presents a Deep Learning (DL)-based framework that offers comprehensive insight into fraud and synthetic review detection in an evolving e-commerce environment. This review paper discusses the importance of DL for detecting online product fake reviews in sentiment analysis using various approaches based on Graph Convolutional Network (GCN), Hierarchical Graph Attention Network (HGAN) Sentiment Majority Voting Classifiers (SMVC), Convolutional Neural Networks with Bidirectional Long Short-Term Memory Networks (CNN-Bi-LSTMs), and a proposed Optimized Bidirectional Encoder Representation Transformers (OBERT) model. This review paper focused on the importance of DL models, particularly the GCN, for effective identification of fake online reviews. This review paper proposed a DL algorithm for fake review detection in online products and demonstrated its practical application in a real-world scenario.
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