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New Technology of Library and Information Service  2016, Vol. 32 Issue (9): 42-50    DOI: 10.11925/infotech.1003-3513.2016.09.05
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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
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[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.

Key wordsLDA Topic model      Similarity      Perplexity      Analysis of Scientific and Technical Information     
Received: 22 February 2016      Published: 19 October 2016

Cite this article:

Guan Peng,Wang Yuefen. Identifying Optimal Topic Numbers from Sci-Tech Information with LDA Model. New Technology of Library and Information Service, 2016, 32(9): 42-50.

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