SENTIMENTAL ANALYSIS OF VACCINATION ON TWITTER

Abstract

Twitter Sentiment Analysis applies sentiment analysis to data in tweets on social media platforms to identify user sentiment. The scope of research in this field has expanded steadily in recent decades. The reason is that the format of tweets is complex and difficult to process. The extremely small Tweet format introduces a whole new set of problems: B. Use of Slang and Acronyms. The COVID-19 coronavirus pandemic continues to impact the health and well-being of people around the world. Our worldview has changed as a result of the pandemic. A vaccination campaign should be carried out among the population to stop the outbreak of the pandemic. However, people are unsure about vaccination, which is cause for concern. This study used machine learning to evaluate public Twitter tweets related to COVID-19 vaccination to determine how users feel about vaccination (ML). Twitter data was analyzed into positive or negative tweets using his three different machine learning (ML) techniques: Support Vector Machine (SVM) and Logistic Regression (LR). The accuracy of the analysis results was 85.3% for the LR algorithm and 87.34% for the SVM.