Sentiment Analysis on Twitter's Big Data Against the Covid- 19 Pandemic Using Machine Learning Algorithms

Abstract

: This paper analyzes users' reactions on Twitter to the COVID-19 pandemic, using machine learning and data mining algorithms to classify tweets according to economic and health fears. A large dataset of tweets is explored, extracted, transformed, loaded, cleansed, and analyzed. The proposed framework improves prediction quality with a proposed dictionary that is used to classify tweets. The study compares four supervised machine learning algorithms and finds that people discuss the pandemic's dangers from economic and health perspectives with equal frequency. The Naive Bayes algorithm achieves the highest percentage of correct predictions.