Sentimental Analysis Of COVID-19 Vaccination Tweets Using Machine Learning Techniques

Sentimental Analysis Of COVID-19 Vaccination Tweets Using Machine Learning Techniques

Sam Manasseh

PG student, Department of MCA,

Dayananda Sagar College of Engineering,

Bangalore, Karnataka, India

sammanasseh310@gmail.com

 Mahendra Kumar B

Assistant Professor, Department of MCA,

 Dayananda Sagar College of Engineering,

Bangalore, Karnataka, India

mahendra-mcavtu@dayanandasagar.edu

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 global community remains affected by the ongoing COVID-19 pandemic, with its profound implications for the health and overall welfare of individuals worldwide. 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. In this study, machine learning techniques were employed to assess the sentiment of public Twitter tweets concerning COVID-19 vaccination. The researchers utilized two distinct ML methods, namely Support Vector Machine (SVM) and Logistic Regression (LR), to analyze the Twitter data and classify the tweets as either positive or negative.

Keywords:Classifier, Twitter, Sentiment Analysis.