Published Paper Details:

Internet based marketplaces like Amazon have changed the way people shop in the entire world giving consumers access to millions of products and services. Nevertheless, with the swift growth of the e-commerce business, fake reviews are another product of excessive growth that confuses consumers and undermines the trust in online platforms. Fake reviews are adept and challenging to be detected and classified as fake reviews. The study of fake review detection and classification methods is well-represented in this survey with particular attention paid to Amazon dataset. The paper examines more conventional machine learning methods, more modern deep learning architectures, as well as hybrid systems, applied to detecting fraudulent reviews. In addition, it discusses the problems, measurement criteria, and the new areas of focus in the domain, providing a considerable opportunity to researchers and practitioners who can develop new ways to increase trustworthiness in online marketplaces.