Study on food safety emergency topic detection model based on semantics

A new method of self-adaptive emergency topic detection model based on semantics is proposed in this paper. We apply the CHI_LDA method to establish the model for the news topics and reports, so as to realize the topic modeling in the semantic feature space. It can resolve the problems of high dimension and sparseness in the feature space and semantic relevance, and improve the time efficiency for LDA method to realize the semantic mapping of the feature space. We improve the traditional Single-pass incremental clustering algorithm by optimizing the updating strategy of the topic model. Meanwhile we establish the topic detector combined with the news topic timing characteristics, and realize the self-adaptive learning of the topic model so as to track the dynamic changes in topic. Experimental results indicate that this method of topic detection has a better performance; it can further improve the effect of topic detection.