Analysis and Detection of Deceptive Product Reviews for E-Commerce Platforms Using Machine learning and Text Mining: A Systematic Literature Review

Online reviews significantly influence modern business and commerce. Consumer decisionmaking processes for online purchases are significantly influenced by user feedback. However, this has led to the rise of fraudulent practices, where individuals or groups manipulate product reviews to serve their own interests. Posting fake reviews is a common deceptive practice used to unfairly promote products. This issue is especially prevalent in e-commerce platforms, where fake reviews can distort customer perceptions and impact purchasing decisions. To tackle this issue, we introduce a system that utilizes machine learning algorithms to identify and flag fraudulent reviews on e-commerce platforms. In our approach, we develop a novel e-commerce application where customers can register, browse products, make purchases, and share their feedback. The system utilizes data science techniques to analyze customer reviews and detect potential instances of fake reviews. This solution aims to enhance trust in online reviews and improve the overall shopping experience by ensuring the authenticity of customer feedback. Keywords— Fake Review Detection, E-commerce Platforms, Machine Learning Algorithms, Naïve Bayes Classifier, Semi-Supervised Learning, Supervised Learning, Results.