Estimating Customer Reviews in Recommender Systems Using Sentiment Analysis Methods

The paper presents a method for estimating unknown user reviews in terms of which specific aspects of a particular item, such as a restaurant, a user would mention in a review that he/she would write about the item and also which sentiments the user would express about these aspects. Unlike the traditional rating-based recommendation methods, the proposed approach estimates user experiences of an item in terms of the most crucial aspects of the item for the user. Therefore, this approach enables more detailed item recommendations to the user. We apply this method to two real-life review datasets from Yelp to evaluate its performance.