Fake Review Detection Of E-Commerce Electronic Products Using Machine Learning Techniques

The rapid growth of internet access has given rise to a digital era. The availability of internet access has pushed almost 70% of the population to switch to internet for their daily needs and accessories. Mainly, E-commerce platforms are being used at a much higher rate than ever before. People who buy from these e-commerce platforms make decisions on whether to buy a product or not solely based on the ratings and reviews of a product that are provided by these platforms. Due to the simple nature of this review system, sellers and even individuals tend to exploit it by writing dishonest reviews with an intention of either boosting its ratings or simply to sabotage it. These fake reviews are aimed at deceiving customers and convince them to buy/deter a certain product. Due to the lack of a robust system to identify real and fake reviews, these spams manage to show up on top. To avoid this problem and provide a more efficient way to filter and provide a more efficient way to reviews. This work focus on designing machine learning model for fake review detection and compare the performance of three different algorithms. As a result of this research work random forest algorithm outperform than other two algorithms. Web based User Interface(UI) designed to remove fake review and display trusted review based on the ranking.