Abstract:

Based on the introduction of the features of time series and labels of the document into latent Dirichlet allocation (LDA) model, an online labeled incremental topic model was presented. Firstly, online labeled incremental topic model realizes the predicate of multilabels on the basis of the optimized label and topic mapping relation and improves the clustering results. Secondly, the online labeled incremental topic model achieves the reasonable correlation of text streams with the help of dynamic dictionary and the optimization calculation of hyperparameter. The experimental results suggest online labeled incremental topic model can improve the decision accuracy of multilabels, optimizing the generalization ability and operating efficiency.

Key words: information processing, latent Dirichlet allocation (LDA) model, natural language analysis, topic model