Sentiment Analysis of data from Social Media Platforms on COVID-19 using Machine Learning Algorithms

Abstract The COVID-19 epidemic has significantly impacted global society, generating a plethora of ideas and data across various digital media platforms. With an eye toward extracting insightful public sentiments, concerns, and responses to the epidemic, this research paper explores the use of sentiment analysis methods to examine COVID-19-related data. We investigate the sentiment expressed in social media posts using cutting-edge natural language processing (NLP) methods to extract insightful information from enormous volumes of textual data. This study offers policymakers, medical experts, and researchers the means to find public attitudes through the suggested method and create focused plans to handle the epidemic.