Analyzing the research trends of COVID-19 using topic modeling approach
Journal of Modelling in Management
; 18(4):1204-1227, 2023.
Article
in English
| ProQuest Central | ID: covidwho-20243948
ABSTRACT
PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.
Business And Economics--Management; COVID-19; Topic modeling; Latent Dirichlet allocation; Literature review; Health impact; Socioeconomic impact; Currency transactions; COVID-19 vaccines; Bibliometrics; Artificial intelligence; Trends; Data mining; Global economy; Health care; Economic conditions; Pandemics; Medical research; Medical equipment; Supply chains; Viruses; Literature reviews; Disease prevention; Mental health; Outdoor air quality; Respiratory diseases; Coronaviruses; Disease transmission
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Journal of Modelling in Management
Year:
2023
Document Type:
Article
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