The traditional sentiment model faces two main problems in analyzing user's emotion of product reviews: 1) the lack of fine-grained emotion analysis for product attributes; 2) the number of product attributes shall be defined in advance. In order to alleviate the problems mentioned above, a fine-grained model for product attributes named User Sentiment Model (USM) was proposed. Firstly, the entities were clustered in product attributes by Hierarchical Dirichlet Processes (HDP) and the number of product attributes could be obtained automatically. Then, the combination of the entity weight in product attributes, the evaluation phrase of product attributes and sentiment lexicon was considered as prior. Finally, Latent Dirichlet Allocation (LDA) was used to classify the emotion of product attributes. The experimental results show that the model achieves a high accuracy in sentiment classification and the average accuracy rate of sentiment classification is 87%. Compared with the traditional sentiment model, the proposed model obtains higher accuracy on extracting product attributes as well as sentiment classification of evaluation phrases.