Topic Extraction Method Based on SLDTM

Guo Xiaoli, Zhou Zilan

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Journal of Northeast Electric Power University ›› 2017, Vol. 37 ›› Issue (5) : 80-86.

Topic Extraction Method Based on SLDTM

  • Guo Xiaoli, Zhou Zilan
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Abstract

Owing to exist the difficult of determine the number of topics and key point of times and accurate interpretation of topics for existing LDA model.There present SLDTM,which fused an improved clustering algorithm to the DTM model and using the tag information for supervised learning in each subset.In this paper,a more reasonable text set segmentation can be achieved because the sliding window size of SLDTM can be changed according to the distribution characteristics of the topics.The number of topics is variable and can be understand easier.experimental results show that compared with the previous topic model,these extracted topics of SLDTM can reflect the important changes of the content and the semantics is clearer.

Key words

Topic Extraction / Topic Model / Tag / Text Processing

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Guo Xiaoli, Zhou Zilan. Topic Extraction Method Based on SLDTM. Journal of Northeast Electric Power University. 2017, 37(5): 80-86

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