COVID19 Sentiment Analysis using Machine Learning Classification Algorithms

Abstract

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis. The diverse use of social networking sites, like Twitter, speeds up the process of sharing information and having views on community events and health crises COVID-19 has been one of Twitter's trending areas. The Twitter messages created via Twitter are named Tweets. In this paper, we identify public sentiment associated with the pandemic using Coronavirus-specific Tweets and Python, along with its sentiment analysis packages. We provide an overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. This research provides insights into Coronavirus fear sentiment progression, associated methods, limitations, and different opportunities. In this project, we have designed a Sentiment analysis System that would identify the sentiment of a tweet and classify it into one of the five classes they include:”ExtremelyPositive”,“Positive”,”Neutral”, ”Negative” and “Extremely Negative”.