Abstract
The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies. The Author(s) 2020
| Original language | English |
|---|---|
| Pages (from-to) | 304-320 |
| Journal | Journal of Information Science |
| Volume | 48 |
| Issue number | 3 |
| Online published | 31 Aug 2020 |
| DOIs | |
| Publication status | Published - Jun 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Keywords
- COVID-19
- latent Dirichlet allocation
- literature analysis
- MERS
- SARS
- text mining
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
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