Investigating diseases and chemicals in COVID-19 literature with text mining

Abstract

Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, and lack of data-driven tools. This research has collected 9298 papers representing COVID-19 research published through May 5, 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling that provided 25 categories showing associations between the two overarching categories. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

Keywords

• Chemical
• Drug
• Disease
• Symptom
• Text mining
• SARS-CoV-2