Analysis of the Sentiment of Tweets Regarding COVID-19 Vaccines Using Natural Language Processing and Machine Learning Sectionification Algorithms

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The unique Coronavirus pandemic of 2019 (called COVID-19 by the globe Health Organisation) has exposed several governments throughout the globe to risk. The Covid-19 epidemic, which had previously only affected the Chinese population, is now a major worry for countries all over the globe. Additionally to the obvious health effects of COVID-19 epidemic, this study reveals its repercussions on the worldwide economy. The research went on to talk about how they analysed public opinion and learned new things about Covid-19 vaccinations by using content Analytics and sentiment evaluation in Natural Language Processing (NLP) using content from Twitter. To categorise and analyse the outcomes, researchers used two machine learning algorithms: logistic regression (LR), random forest, decision tree, and convolutional neural networks (CNNs). To better identify public opinion, several preprocessing methods were used and categorised responses into neutral, positive, and negative categories. The public's opinion on Covid-19 vaccinations is 31% favourable, 22% negative, and 47% neutral, according to the results of the emotion section distribution. CNN achieved 98% accuracy, according to the tested machine learning algorithms.