The Research About Topic Extraction Method Based on the DTS-ILDA Model
Because the existing LDA model is difficult to determine the number of topics and the key point of time, it is difficult to explain the topic result accurately. In this paper, the DTS-ILDA model is proposed, which fused an improved clustering algorithm into the DTM model, and label information is used for supervised learning on each subset. The size of the sliding window varies according to the topic distribution characteristics in this model. Text segmentation can be achieved more reasonable. The number of topics is also variable and easy to understand. The experiment shows that this method can effectively find the time points of important changes in the topic content, and prevent insignificance topics. It can reduce the related interference of the wrong topics and dig out the exact deep relationship at the same time.
KeywordsModel Time window segmentation Label Dynamic supervision
This work is supported by the Science and Technology Development Project of Jilin Province of China (20180201092GX); Science and Technology Development Project of Jilin Province of China (20180101335JC). The project of teaching reform of higher education in Jilin Province of China (NO. 4 in 2017).
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