Mining And Evolutionary Analysis Of Thematic Groups Of COVID-19 Research Literature

With the global spread of the COVID-19 pandemic,a vast number of research papers have emerged in just a few years,making it difficult for researchers to have a comprehensive grasp of their own research areas,analyze the value of interdisciplinary research,and for policy-makers to accurately consult relevant literature to make informed decisions.In this context,it is of great significance for researchers and policy-makers to mine the thematic clusters of COVID-19 research literature,understand the evolution of topics,and fully grasp the cutting-edge and interdisciplinary fields.This abstract is for a master’s thesis,please translate it into English,paying attention to the appropriate use of academic terminology.This study aims to reveal the key thematic clusters and evolutionary trends of COVID-19 research through an in-depth analysis of a large number of Chinese and English research papers on COVID-19,thereby providing valuable references for coping with the pandemic,guiding future research directions,and policy-making.In this study,co-occurrence network analysis and topic modeling analysis methods are adopted to analyze 11,500 Chinese articles from the CNKI database and37,000 English articles from the Web of Science,to gain insights into the development dynamics and topic evolution trends in the field of COVID-19 research.Firstly,this paper uses Citespace to conduct co-occurrence network analysis on the literature,revealing the core researchers,core research institutions,keywords,and their relationships in different thematic clusters.Secondly,the LDA model and DTM model are employed for topic modeling analysis of the literature,to mine research thematic clusters and explore their evolution.By comparative analysis,the core researchers,core research institutions,research hotspots,interdisciplinary fields,and their time-evolving information in COVID-19 research based on different thematic clusters are revealed.In summary,this study provides a comprehensive and in-depth perspective on the mining and evolution analysis of thematic clusters in COVID-19 research literature,contributing to the understanding of the development status and future trends in the field of COVID-19 research.This will offer valuable reference information for relevant researchers and policy-makers,helping to promote the in-depth development of COVID-19-related research.