Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA.

Change log
Gupta, Akash 
Aeron, Shrey 
Agrawal, Anjali 
Gupta, Himanshu 

Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.

COVID-19, LitCovid, Pubmed, latent dirichlet allocation, natural language processing, topic model, trends
Journal Title
Front Digit Health
Conference Name
Journal ISSN
Volume Title
Frontiers Media SA