Abstract: As one of the most widely used models in the field of text topic recognition, LDA simplifies the assignment of the same weight to words based on the assumption of bag-of-words model, which makes the topic distribution inclined to high-frequency words, as well as affects the semantic coherence of the recognized topics. This paper proposes an improved LDA algorithm based on graph mining, named GoW-LDA, which firstly builds a semantic graph model based on the co-occurrence of feature word pairs in the text, then uses the weighting degree of nodes in network statistical features to integrate the semantic structure characteristics and relevance of the text into the LDA topic modeling in the form of weight correction. Experimental results show that, compared with traditional LDA and TF-IDF-based LDA, GoW-LDA can greatly reduce the complexity of topic models, improve the PMI of topic recognition, and effectively reduce the training time, which provides for a new solution idea text topic recognition.

Key words: text topic recognition, graph mining, LDA(Latent Dirichlet Allocation)