Mapping the Linkage Between Covid-19 and Finance Research Using Structural Topic Modelling: A Machine Learning Perspective

Inani, Sarveshwar Kumar, Mohnot, Jitesh, Pradhan, Harsh, Nagpal, Gaurav and Nagpal, Ankita (2025) Mapping the Linkage Between Covid-19 and Finance Research Using Structural Topic Modelling: A Machine Learning Perspective. In: 2025 International Conference on Technology Enabled Economic Changes, InTech 2025, 27-28 February 2025, Tashkent.

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Abstract

There is a substantial body of scholarly literature on finance research conducted during the COVID-19. However, a comprehensive and systematic overview of the relevant literature has not yet been established. Therefore, the present study conducts bibliometric analysis and structural topic modeling on a dataset of 3,435 articles extracted from Scopus between 2020 and 2023. It studies publication patterns, significant journals and institutions publishing papers on finance research during the COVID-19 crisis. Seven thematic clusters in finance research including accounting, public finance, banking, corporate financing, market volatility, sentiment analysis, and governance - have been identified by structural topic modelling. These findings aid in comprehending the major COVID-19 related financial research themes. The study has also recommended a future research agenda.

Item Type: Conference or Workshop Item (Paper)
Keywords: Machine Learning | Structural Topic Modelling | Text Mining | Finance Research | COVID-19
Subjects: Social Sciences and humanities > Social Sciences > Social Sciences (General)
JGU School/Centre: Jindal Global Business School
Depositing User: Mr. Luckey Pathan
Date Deposited: 22 Dec 2025 09:40
Last Modified: 22 Dec 2025 09:41
Official URL: https://doi.org/10.1109/InTech64186.2025.11198293
URI: https://pure.jgu.edu.in/id/eprint/10531

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