Articlebronze2021

Using text mining to glean insights from COVID-19 literature

23 citations0/0 files storedJournal of Information Science

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Computational and Text Analysis Methods scientometrics and bibliometrics research Advanced Clustering Algorithms Research

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.

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2014big data @ work 2010Latent Semantic Analysis: five methodological recommendations 2020Scientists are drowning in COVID-19 papers. Can new tools keep them afloat?2013Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS 2020Timely research papers about COVID-19 in China 2017Exploring research on quality and reliability management through text mining methodology

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2022Analyzing the research trends of COVID-19 using topic modeling approach

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