Sentiment Analysis on Covid-19 Vaccination Using Machine Learning Techniques

Abstract

In this research work, we have performed Machine Learning and Lexicon Based Techniques to identify and analyze user's expression or opinion on covid-19 vaccination from social media platform that is Twitter and acquainted the bulk tweets from 01 June 2021 to August 2021 using various twitter hashtags. Machine learning based Classifiers are used for investigating the evaluation performance of Algorithms. Real time datasets and machine Learning Algorithms are compared with Best Data classification Evaluation based on the size of train data also another approach is to investigating the polarity by using Lexicon Based approach for this Bing Liu Lexicons and Stanford University Lexicons are used. The global pandemic has created the medical emergency and stops the many regular activities. The whole world in the lockdown or quarantine to because Coronavirus disease. Among them, Covaxin, Covishield, Pfizer, Moderna and SputnikVare popular. Universally publicare articulating opinions on protection and success of the vaccines on social media. Research article shows, such tweets are collected from developer Application Management using a Twitter API. Unprocessed tweets are kept and preprocessed through Machine Learning techniques. Users opinion are predicted using a Classifiers Decision Tree, Support Vector Machine, K NN Algorithm and Naïve Bayes. Comparative machine learning classifiers study here comparative analysis is got highest accuracy of 97% for Decision tree with Covaxin dataset, Support vector machine with 94% for SputnikV, Naïve Bayes got highest accuracy of 95 for Covishield dataset and KNN got Highest accuracy of 96% for Covaxin. The Lexicon Based polarity classifies the score into three users opinions, positive, negative, and neutral. Result shows that, Covaxin shows 28.