User reported experiences and opinions are used by peers to make decisions about where to go and what to buy. Unfortunately, not all users or opinions are honest. Many opinions are fabricated and may be submitted by automated systems or by people who are recruited by businesses and search engine optimizers to write good reviews. Such reviews and ratings are called spam reviews. These are misleading for users and troublesome for honest businesses. While most current efforts to tackle this problem are focused on spam review detection, in this paper we focused on detecting authentic and valuable reviews for a front-end application that reorders the reviews. In this manner, we have identified several features based upon the content of the reviews as well as identifying behavioural features of reviewers to pinpoint useful reviews with 80% accuracy.