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Using text mining to glean insights from COVID-19 literature.

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This study used text clustering to analyze COVID-19 research, revealing hidden themes and reducing the search space for researchers. This method helps scientists efficiently navigate the vast body of COVID-19 literature.

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Abstract clusteringCOVID-19 Open Research Data setexpectation–maximisation algorithmsingular value decompositiontext clustering

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Area of Science:

  • Bibliometrics
  • Computational Linguistics
  • Infectious Disease Research

Background:

  • The rapid increase in COVID-19 publications necessitates efficient information retrieval methods for researchers.
  • Existing search methods may not effectively identify emerging research themes within the vast COVID-19 literature.

Purpose of the Study:

  • To develop and evaluate a text clustering approach for analyzing COVID-19 research abstracts.
  • To demonstrate the utility of text clustering in uncovering latent research topics and streamlining literature review processes.

Main Methods:

  • Analysis of 83,264 COVID-19 research article abstracts.
  • Application of singular value decomposition (SVD) for dimensionality reduction.
  • Utilisation of the expectation-maximisation (EM) algorithm for text clustering.

Main Results:

  • Text clustering successfully identified distinct research themes within the COVID-19 literature.
  • The clustering approach significantly reduced the volume of literature requiring manual review.
  • Emerging and niche research areas were highlighted through the analysis.

Conclusions:

  • Text clustering is an effective method for navigating and understanding the large-scale COVID-19 research output.
  • This approach aids researchers in efficiently identifying relevant studies and potential areas for future investigation.