Research on text evaluation based on natural language processing and GSDMM topic model

Driven by natural language processing technology, topic models have shown a wide range of application value in fields such as text analysis, classification and clustering. The GSDMM topic model has been widely recognized in the field of topic modeling due to its ability to automatically infer the number of clusters, fast convergence, and efficient processing of sparse and high-dimensional short texts. This paper aims to explore the text evaluation research based on natural language processing and GSDMM (Gibbs Sampling and Distributed Memory Model) topic model, and carries out relevant practice in the text evaluation of project-based teaching course.