Research on Topic Discovery and Evolution Trend based on Temporal Keyword Characteristics Analysis

Authors

Shuqing LI, College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023
Juntao ZHU, Hunan Institute of Scientific and Technical Information, Changsha 410001
Wan WANG, College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023

Keywords

timing keywords; theme evolution; keyword extraction; visual analysis

Abstract

[Purpose/significance]Excavating the research topics in a large number of articles, sorting out the evolution context and correlation of the research topics, predicting the frontier hot spots of the topics can be helpful to enhance the scientificity and vividness of the evolution results.[Method/precess]This paper puts forward the concept of time series influence factor as an important feature in keyword extraction, uses the method of time window to mine and identify topics by using topic model, and makes visual analysis. By applying time series model in the field of deep learning, the purpose of predicting topic popularity is achieved.[Result/concluson]It is verified that the keyword extraction integrating temporal features can improve the effect of the topic model. Through visualization, not only the change trend of the overall topic popularity can be observed, but also the evolution of the topic content in each time period can be analyzed, its splitting and merging trend can be also explored.

First Page

57

Recommended Citation

LI, Shuqing; ZHU, Juntao; and WANG, Wan (2023) "Research on Topic Discovery and Evolution Trend based on Temporal Keyword Characteristics Analysis," Scientific Information Research: Vol. 5: Iss. 2, Article 6.
Available at: https://eng.kjqbyj.com/journal/vol5/iss2/6

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