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.

本研究的目的是对 COVID-19 研究文章进行基于文本聚类的分析。由于已发表的 COVID-19 研究文章激增，研究人员需要一种方法来减少他们必须搜索的文章数量才能找到与其专业知识相关的材料。该研究分析了与 COVID-19 相关的研究文章中的 83,264 篇摘要。使用奇异值分解 (SVD) 和期望最大化 (EM) 算法分析文本数据。结果表明，文本聚类既可以揭示与 COVID-19 相关的已发表文献中隐藏的研究主题，又可以减少研究人员需要搜索以找到与其感兴趣领域相关的材料的文章数量。