Among sentiment analysis tasks, review rating prediction is more helpful than binary (positive and negative) classification, especially when the consumers want to compare two good products. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text feature are modeled as a three-dimension tensor. The tensor factorization technique is employed to reduce the sparsity and complexity problems. The experiment results demonstrate the effectiveness of our model. We achieve significant improvement as compared with the state of the art methods, especially for the reviews with unpopular products and inactive reviewers.