Identifying Optimal Topic Numbers from Sci-Tech Information with LDA Model
Guan Peng1,2,Wang Yuefen1()
1School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China 2College of Applied Mathematics, Chaohu University, Hefei 238000, China
[Objective] This paper tries to identify the optimal number of topics for the Latent Dirichlet Allocation (LDA) model to analyze scientific and technical information. [Methods] First, we used the topic similarity to measure the differences among the latent topics. Second, we proposed a method determining the optimal topic numbers and tried to utilize this model to documents from Chinese literature in the field of new energy. [Results] The proposed method achieved higher precision ratio and higher F-score in topic extration, which improved the performance of literature recommendation systems. [Limitations] We did not examine the new mothod with other datasets, such as microblog posts and XML documents. [Conclusions] The proposed method could identify more recognizable topics and improve the performance of scientific and technical literature recommendation systems.
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