Abstract
This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, the network analysis helps develop strategies for a sustainable response to and the management of national crises through identifying clusters of words with similar structural equivalence.
| Original language | English |
|---|---|
| Article number | 4123 |
| Journal | Sustainability (Switzerland) |
| Volume | 13 |
| Issue number | 8 |
| DOIs | |
| State | Published - 2 Apr 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 17 Partnerships for the Goals
Keywords
- COVID-19
- Crisis management
- Network analysis
- Pandemic crisis
- Text mining
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