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In Recent Years, 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 An 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. 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. Then, We 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 Helps To Improve The Recommendation Performance