Textual Dissection of Live Twitter Reviews on Corona Vaccines using Various Machine Learning Algorithms

ABSTRACT

The most popular hash tag on Twitter in 2020 was #COVID19 Vaccination, which got roughly 400 million notices. In this paper, we examine a worldview for unearth the feeling about COVID-19 inoculations among the public from Twitter. After obtaining the misconceptions and ideas in circulation, we suggest a solution for the same through Machine Learning algorithms. Twitter is a well known microblogging social media website where users distribute their perspectives on any topic(s). The ideology of textual dissection describes how people think about a text. It's the process of categorising tweets into positive and negative groups. Tweepy and TextBlob are Python libraries that can be used to extract and classify Tweets using Machine Learning methods including Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree. The goal is to make analysis, summarization, and classification as straightforward as possible. These computations comprise a positive, negative, or neutral assessment of Twitter data. In light of public perception, we hypothesize the best immunization feasible with maximum antibodies based on public perception through opinion research. © 2022 IEEE.