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In the last recent years, the customer reviews has attracted an increasing interest by researchers and companies thanks to the advancement in E commerce and business applications. The ability to successfully decide whether a review will be helpful to other customers and thus give the product more exposure is vital to companies that support these reviews, companies like Google, Amazon and Yelp! The problem is that some customers are giving rating in conflict with their comments obliging other reviewers to read the comments to understand the reason of this contradiction. Sentiment analysis is the process of automatically identifying opinions expressed in text on certain subjects. The accuracy of sentiment analysis has a direct effect on decision making in both business and government. Also, recommender system is one of the hot topics in the field of big data, especially with the development and growth of the World Wide Web. This paper presents a survey covering the methods in the field of reviews rating prediction. We also propose a hybrid architecture that combines techniques from sentiment analysis and recommender systems fields, to develop a solution that has the ability to predict rating of products while taking into consideration the analysis of sentiments included in the products reviews. Subject Categories and Descriptors: H.3.1 [Content Analysis and Indexing]: H.2.8 [Database Applications]: Data mining; General Terms: Sentiment Analysis, Opinion Mining, Recommender Systems Prediction of Reviews Rating: A Survey of Methods, Techniques and Hybrid Architectures