COVID-19 literature trend and topics analyses using intelligent text mining

Abstract

With the widespread pandemic, numerous scientific papers on COVID-19 have been published at an unprecedented rate. Keeping up with current trends and main topics of publications on the pandemic seems an impossible task. On the other hand, advanced techniques using big data analytics, machine learning, and automated text mining approaches can be used to obtain an exploratory overview of the COVID literature. This study adopts Asmussen and Møller’s smart literature review framework to analyze some COVID-19 publications in the earlier period of the pandemic gathered from the COVID-19 Open Research Dataset. The results show that the smart literature review framework is promising to identify hidden research themes or patterns in the published literature related to COVID-19.