Sentimental Analysis of COVID-19 Twitter Data Using Machine Learning

Corona virus is considered as a scourge all over world by which most of the people got infected. Since the virus was spreading very quickly governments had to bring in very tough rules such as lock down. Lock down disrupted almost all sector like education, business etc.… During lock down and after the lock down people have used social media as their platform to express the opining regarding the pandemic. Tweeter is one such social media where people had shared their thoughts on the pandemic. In this study tweets which are made by the public is considered to analyses the sentiments. And in this research machine learning models are used to classify the data into five different classes namely positive, negative, neutral, extremely positive, and extremely negative. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes are the ML models used to classify the sentiment and are achieving on accuracy of 99%, 99%, 97%, 94%, and 73%, respectively. Further precision, recall, F1-score, and world clod are calculated for each and every class.