Research Progress on Topic Evolution of Scientific and Technical Literatures Based on Text Mining

Liang Shuang; Liu Xiaoping

doi:10.13266/j.issn.0252-3116.2022.13.013

Library and Information Service >

2022 , Vol. 66 >Issue 13: 138 - 149

DOI: https://doi.org/10.13266/j.issn.0252-3116.2022.13.013

Research Progress on Topic Evolution of Scientific and Technical Literatures Based on Text Mining

Liang Shuang ,
Liu Xiaoping

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1. National Science Library, Chinese Academy of Sciences, Beijing 100190;
2. Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190

Received date: 2022-01-12

Revised date: 2022-05-01

Online published: 2022-07-06

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Abstract

[Purpose/Significance] This paper classifies, summarizes and concludes the various methods used in the analysis of topic evolution by sorting out the research related to the topic evolution of scientific and technical literatures based on text mining at home and abroad, and proposes the shortcomings of the existing research to provide new ideas and reference significance for the study of topic evolution. [Method/Process] According to the general process of topic evolution research by domestic and foreign scholars, this paper compared and summarized the advantages and disadvantages of various models, indicators and methods used in the three levels of analysis: data set selection and object analysis, topic identification research and topic evolution research (topic evolution time sequence analysis, topic intensity evolution analysis and topic content evolution analysis). Finally, pointing out the limitations of existing research and putting forward the prospect for the future development. [Result/Conclusion] At present, the research has a certain scale and a relatively mature analysis system, but there are still the following shortcomings: the data source is single; the drawbacks of LDA and related extended models need to be further overcome; the lack of exploration and application of other machine learning and deep learning algorithms; evolutionary analysis methods need to be combined and complemented with each other. In the future, we should make corresponding improvements and in-depth exploration for the above problems.

Key words： text mining; topic model; topic identification; topic evolution

Cite this article

Liang Shuang , Liu Xiaoping . Research Progress on Topic Evolution of Scientific and Technical Literatures Based on Text Mining[J]. Library and Information Service, 2022 , 66(13) : 138 -149 . DOI: 10.13266/j.issn.0252-3116.2022.13.013

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