Topic evolution based on LDA and HMM and its application in stem cell research

Qingqiang Wu · Caidong Zhang · Qingqi Hong · Liyan Chen

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Computational and Text Analysis Methods scientometrics and bibliometrics research Topic Modeling

Abstract

This paper analyses topic segmentation based on the LDA (Latent Dirichlet Allocation) model, and performs the topic segmentation and topic evolution of stem cell research literatures in PubMed from 2001 to 2012 by combining the HMM (Hidden Markov Model) and co-occurrence theory. Stem cell research topics were obtained with LDA and expert judgements made on these topics to test the feasibility of the model classification. Further, the correlation between topics was analysed. HMM was used to predict the trend evolution of topics over various years, and a time series map was used to visualize the evolutional relationships among the stem cell topics.

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References

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