© 2020, Boom Lemma Publishers. All rights reserved. A novel coronavirus, SARS-CoV-2, is known as the coronavirus disease pathogen for 2019 (COVID-19). The COVID-19 epidemic has spread across the world and has become an international public health emergency. This paper aims to analyze and visualize the influence of coronavirus (COVID-19) in the world by executing such algorithms and methods of machine learning in sentiment analysis on the tweet dataset to understand very positive and very negative opinions of the public around the world. This reveals that Naive Bayes 'machine learning approach has been produced better execution, and it has been regarded as the basis for basic learning. This also brings out another ensemble technique that uses sentiment score as the input function for the classifiers in machine learning, SVM, Max Entropy, Decision Tree, Boosting, and Random Forest. As a result, the LogitBoost, a blended approach, performed better with accuracy of 74%.