Article’s

With the rapid growth of e-commerce platforms, online reviews have become a critical factor influencing consumer decisions. However, the presence of deceptive or fake reviews poses significant challenges for both users and businesses. This paper presents a comparative review of various fake review detection techniques, including traditional machine learning approaches, deep learning models, and transformer-based architectures. A detailed analysis of representative studies is conducted based on methodology, datasets, and performance metrics. The study highlights the evolution of detection techniques from feature-based models to advanced context-aware systems. Additionally, a machine learning-based approach using TF-IDF and Support Vector Machine (SVM) is discussed to demonstrate practical implementation. Key research gaps such as generalization, computational complexity, and interpretability are identified, along with potential directions for future work. The findings provide valuable insights for developing more robust and scalable fake review detection systems.