How to automatically extract valuable opinion information from enormous texts has become an important technical challenge. Currently, most opinion mining methods are based on the assumption that topics are independent of each other. However, there are complicated inherent relationships between topics. In order to solve the above problems, based on standard CTM model, the paper proposes a hybrid correlated topic model that mixes topic with sentiment to perform opinion mining. Considering the topic correlation of documents, opinion characters and potential sentiment tendency are analyzed. Based on these results, sentiment polarity of the whole review and each topic are obtained. The simulation results verify the validity of the proposed model. R language is also used to visualize the experimental results.