A Machine Learning Approach to Track COVID-19 Pandemic using Sentiment Analysis

Abstract

Coronavirus disease or COVID-19 is one of the most frightening and infectious diseases of the twenty-first century. Since the outbreak of COVID-19 in Wuhan, China, numerous researches are conducted in this sector. At the preliminary stage, there was not sufficient numeric data for research but when we consider the text data such as trending topics of Social Media or patients sharing experiences about their symptoms, we get enough data to ace the navigation of the Coronavirus (SARS-CoV-2). Keeping aside the health complications related to COVID-19, there also has been huge public panic following the pandemic. Sentiment analysis helps to learn the emotions of a vast number of people about any particular topic. In this paper, we have used sentiment analysis methods to observe the public reaction to the COVID-19 pandemic and people’s experience of the ongoing vaccination process. Machine Learning-based (ML-based) classification algorithms are implemented for text classification. Finally, the accuracy of the classification models is also calculated for further prediction.