Using text mining to glean insights from COVID-19 literature-Reference-Cited by-同舟云学术

Using text mining to glean insights from COVID-19 literature

Published:2021-03-16 Issue: Volume: Page:016555152110016
ISSN:0165-5515
Container-title:Journal of Information Science
language:en
Short-container-title:Journal of Information Science

Author:

Anderson Billie S¹²^ORCID

Affiliation:

1. Department of Marketing and Supply Chain Management, University of Missouri Kansas City, USA

2. Henry W. Bloch School of Management, University of Missouri Kansas City, USA

Abstract

The purpose of this study is to develop a text clustering–based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to search through to find material relevant to their expertise. The study analyzes 83,264 abstracts from research articles related to COVID-19. The textual data are analysed using singular value decomposition (SVD) and the expectation–maximisation (EM) algorithm. Results suggest that text clustering can both reveal hidden research themes in the published literature related to COVID-19, and reduce the number of articles that researchers need to search through to find material relevant to their field of interest.

Publisher

SAGE Publications

Subject

Library and Information Sciences,Information Systems

Link

http://journals.sagepub.com/doi/pdf/10.1177/01655515211001661

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