The topic and sentiment unification model (Reverse-Joint Sentiment/Topic Model;Joint Sentiment/Topic Model) can effectively extract information of topic and sentiment simultaneously and receives wide attention in the field of sentiment analysis

because it does not consider the relationship between the overall distribution and local distribution

so the classification performance is not good and stable.This paper proposed the multi-grain topic and sentiment unification model (MG-R-JST;MG-JST) by taking into account both grains on sentiment/topic distributiondocument-level and local-level.MG-R-JST/MG-JST generated the sentiment/topic of words on the effect of the document-level and local-level distribution.we used Gibbs sampling for model inference and showed the process.Experiments on the dataset of MR and MDS demonstrate the effectiveness of the proposed method

and the classification performance is better and more stable than the topic and sentiment unification model.