Summary of Text Categorization based on Maximum Entropy Model

Since 1990s, the maximum entropy model has been used in text categorization and achieves good results in Natural Language Processing since its framework and algorithm were established. On the basis of the Maximum Entropy Model, scholars improve it and make a more in-depth study. Using Maximum Entropy Model for text sentiment categorization has become a hot research topic in recent years. In this paper, the application of Maximum Entropy Model in text categorization is analyzed and classified into three categories: text categorization based on the original Maximum Entropy Model, text categorization based on improved Maximum Entropy Model and text emotion categorization based on Maximum Entropy Model. The authors consider that the existing text categorization using Maximum Entropy Model is to classify the text into a certain category directly, but not to give the probability that a text belongs to a category based on characteristic of Maximum Entropy Model. Therefore, the future research focus will be on the use of the maximum entropy model in t fuzzy classification information.