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Agricultural Library and Information ›› 2019, Vol. 31 ›› Issue (4): 19-28.doi: 10.13998/j.cnki.issn1002-1248.2019.03.19-0342

• Research paper • Previous Articles     Next Articles

Research of Topics Discovery and Tech Evolution Based on Text Preprocessed LDA Model

WANG Li1,2, SHEN Xiang1,2   

  1. 1.National Science Library, Chinese Academy of Sciences, Beijing 100190, China;
    2.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
  • Received:2019-04-24 Online:2019-04-05 Published:2019-06-21

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Abstract: [Objective] Computational science and Data Science are inspiring the intelligent analysis and information service today. Machine learning text analysis methods is changing the traditional analysis methods. This article discuss the benefits of unsupervised learning approaches in patent text mining. [Methods] Patent data of SiC industry were preprocessed by filter model based on NLTK Toolkit to identify the tech terms and then clustered based on Latent Dirichlet Allocation model to find the latent topics which were visualized. Based on group operation Top terms ranked by tf-idf through every year were used to reveal the R&D focus evolution. [Results] This research offers a demonstration of the proposed method based on 43,621 SiC patents. The results show 28 Research and Development topics with tech terms in SiC industry and present a Research and Development focus evolution based new emerging terms of every year which provides a clue for more detail analyses later. Finally,we discuss the clues for the R&D focus in the SiC industry.[Limitation]Multi Topics for documents were not compared for the R&D focus evolution in this article. That will be discussed in future. [Conclusions]The results show a efficent way to find technology focus evolution from a large scale text data.

Key words: LDA model, tech evolution, preprocessed text, visualization, automatic term identification

CLC Number: 

  • TP393
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