The Joint Sentiment/Topic (JST) model can extract both the topic and the sentiment from the text, but the existing JST model mainly focuses on textual content, without considering the user characteristics, which may lead to demographic and event biases in sentiment mining reports. The Joint-User Sentiment/Topic (JUST) model was proposed. The main improvement of the JUST model was that the user characteristics were added to the model, a linear function of the user characteristics corresponding to the document was used as a priori of the document-emotional distribution, so the model could get emotional tendencies of different topics from customer with different characteristics. The validity of the JUST model was tested on the datasets of 13252 automobile review from autohome.com (www.autohome.com.cn). The experimental results show that the accuracy of the sentiment classification of the JUST model is higher than those of the JST model and TSMMF (Topic Sentiment Model based on Multi-feature Fusion) model. The topic and sentiment differences between users with different characteristics were also compared.