COVID-19 Kaggle Literature Organization

TL;DR

This paper presents a machine learning-based approach to organize and visualize COVID-19 related scientific literature, making it easier to navigate and identify related research topics using the CORD-19 dataset.

Contribution

It introduces a novel method for organizing COVID-19 literature through clustering and visualization techniques, facilitating better navigation of the rapidly growing research corpus.

Findings

Effective grouping of papers by topics

Improved visualization of COVID-19 research landscape

Publicly available proof of concept

Abstract

The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature, there arose a need for organizing those documents. We describe an approach to organize and visualize the scientific literature on or related to COVID-19 using machine learning techniques so that papers on similar topics are grouped together. By doing so, the navigation of topics and related papers is simplified. We implemented this approach using the widely recognized CORD-19 dataset to present a publicly available proof of concept.