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Using text mining to glean insights from COVID-19 literature

Billie Anderson

2021Journal of Information Science23 citationsDOIOpen Access PDF

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.

Topics & Concepts

Coronavirus disease 2019 (COVID-19)Cluster analysisField (mathematics)Data scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakComputer scienceInformation retrievalValue (mathematics)Singular value decompositionArtificial intelligenceMathematicsMedicineMachine learningPure mathematicsVirologyInfectious disease (medical specialty)DiseaseOutbreakPathologySentiment Analysis and Opinion Mining
Using text mining to glean insights from COVID-19 literature | Litcius