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A comparative study of feature extraction methods from user reviews for recommender systems

The Recommender systems technology is being massively exploited by e-commerce giants to enhance the shopping experience of their clients which in turn helps in improving the sales of the company. Most of the recommender systems in use today are based on Collaborative Filtering (CF) in which the known preferences of a group of users are used to make recommendations or predictions for the unknown preferences of other users. Although these ratings communicate about the quality of the product, they almost most of the times fail to express the reason behind people believing the product to be of a particular quality. This information can be inferred if analyze the information rich textual reviews written by the users. In the current work, an attempt is made to study and implement various methods described in literature, to mine the product features from the user reviews associated with the product. A comparative study is presented at the end to appreciate the performance of the methods.