PREDICTION OF RATING BASED ON SOCIAL SENTIMENTS AND REVEIWS OF THE USER

Master of Computer Application

NATIONAL CONFERENCE on VLSI, COMMUNICATION AND SIGNAL PROCESSING (NCVCS-2017)

We have seen a flourish of review websites which presents a great chance to share our viewpoints for various products we purchase. We are facing the information overloading problem. How to mine valuable information from reviews to understand a user’s preferences and make an accurate recommendation [1] is crucial. Traditional recommender systems (RS) considers some factors, such as user’s purchase records, product category, and geographic location. In this work, we propose a sentiment-based rating prediction method to improve prediction accuracy in recommender systems. Firstly, we propose a social user sentimental measurement approach and calculate each user’s sentiment on items/products. Secondly, we not only consider a user’s own sentimental attributes but also take interpersonal sentimental influence into consideration. We also consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers’ comprehensive evaluation. At last, we fuse three factors-user sentiment similarity, interpersonal sentimental influence, and item’s reputation similarity into our recommender system to make an accurate rating prediction. We conduct a performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp. Our experimental results show the sentiment can well characterize user preferences, which help to improve the recommendation performance.

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