Fake Reviews Detection using Support Vector Machine

One of the fastest expanding business categories in the world today is internet shopping. People nowadays buy a lot of things from internet shopping sites. Customers can buy a better quality products based on the reviews given by previous buyers of the products. Reviews includes text reviews, ratings and smileys. On a product review there are hundreds of reviews in which some of the reviews would be fake reviews. Opinion mining from natural languages is a difficult method for evaluating customers' sentiments, but sentiment analysis provides the best answer. It provides crucial data for decision-making in a variety of fields. So, we propose a fake reviews detection system using support vector machine which detect the fake reviews of the products. The primary goal is to suggest higher-quality products to the user. We use the support vector machine algorithm to classify the reviews into positive and negative groups. Finally fake reviews are predicted which are posted by the users. The reviews are grouped as negative, positive and neutral. In this system, only purchased users can post the reviews and duplicates are verified based on user id and booking id. Genuine reviews are considered for product recommendation