Year: 2021
Type: article
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.
Source: Journal of Information Science
Author: Billie S Anderson
Institution: University of Missouri–Kansas City
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W3137094842