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New Technology of Library and Information Service  2016, Vol. 32 Issue (11): 20-26    DOI: 10.11925/infotech.1003-3513.2016.11.03
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Extracting Topics of Computer Science Literature with LDA Model
Yang Haixia,Gao Baojun(),Sun Hanlin
Economics and Management School, Wuhan University, Wuhan 430072, China
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[Objective] This paper employs text mining technology to automatically identify research topics from large amounts of scientific literature and then detects future trends. [Methods] First, we used the LDA model to find both topical prevalence and contents of articles published by the top ten computer science journals in China. Second, we described the evolution of major topics with the help of publishing dates. [Results] We extracted 18 topics from 29, 621 computer science papers and then identified 7 trending topics as well as 6 less popular ones. [Limitations] Our study did not include papers published overseas by Chinese authors. [Conclusions] The proposed method could help us learn the evolution of computer science research and then grasp the emerging trends.

Key wordsComputer science      LDA      Topic mining      Topic prevalence      Document cluster     
Received: 02 June 2016      Published: 20 December 2016

Cite this article:

Yang Haixia,Gao Baojun,Sun Hanlin. Extracting Topics of Computer Science Literature with LDA Model. New Technology of Library and Information Service, 2016, 32(11): 20-26.

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