Rating Prediction based on Social Sentiment from Textual Reviews

 Rating Prediction based on Social Sentiment from Textual Reviews

 We have witnessed a flourish of review websites. It presents a great opportunity to share our viewpoints for various products we purchase. However, we face the information overloading problem. How to mine valuable information from reviews to understand a user’s preferences and make an accurate recommendation is crucial. Traditional recommender systems (RS) consider some factors, such as user’s purchase records, product category, and geographic location. In this work, we propose a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems.