In topic evolution and tracking, as the size of time slices and the K value of the topic model are fxed, it is hard to locate important time turning points, which is prone to error topic correlation in the evolutionary analysis. To solve the problem, we propose an improved dynamic temporal segmentation-infnite latent Dirichlet allocation (DTS-ILDA) model and an associated fltering mechanism. The model combines an improved dynamic time segmentation algorithm with an infnite latent Dirichlet allocation (ILDA) model to extract topics. Dynamic time segmentation algorithm traverses the data set according to the time sequence, and then uses a contingency table to analysis the distribution of topics to measure the segmentation results and an ILDA model to extract K topics. In addition, an association fltering mechanism is proposed for error prone association in the evolutionary analysis. It removes weak association relationship. Finally, fve evolutionary relationships of right subtopic association are established according to the time sequence relationship. Experiments show that the presented method can effectively fnd important time points when the main content of the topic changes, preventing generation of meaningless topics. It can also reduce error-topic related interference, extracting exact deep relationship between the topics.
