Abstract: Aiming at  the problem that the emotional factors of the public were not considered enough in the existing related theme models, which was difficult to accurately excavate them, and the real-time dynamic evolution of social texts was considered to weaken the clustering ability of the model, the author  proposed a text clustering algorithm based on the dynamic theme emotin model by adding  the emotional layer to the model to extract the polar features of social text emotion, and introducing a prior distribution function. The experiments were carried out by using real COVID-19 Twitter text datasets.  The experimental results show that the performance of the model is better than the baseline model,  and   the discrimination of emotional features is improved, so that  the text theme and the corresponding emotional polarity can jointly generate time nodes, and then  the model has the ability to deal with time evolution.

Key words: dynamic topic emotion model； text mining； , emotional label； time stamp, text clustering, perplexity