Analysing Public Sentiments Regarding COVID-19 Vaccines: A Sentiment Analysis Approach

The COVID-19 pandemic has signified the interconnected nature of our world demonstrating that no one is safe until everyone is safe. The social and economic turmoil caused by the pandemic is devastating and revealing a dramatic loss of human life worldwide and presents a prodigious challenge to food systems, public health, and work worldwide. The vaccination programs are of utmost priority for every institution but there is a clear divide among people on efficacies and application of the offered vaccines. Today, the world has access to high-performance wireless internet due to 5G technologies which can enable systems to fetch billions of records from social media within a blink of an eye. The internet revolution has opened a new door of opportunities. This study aims to come up with a system that can utilize 5G technologies to access the data from social media to create awareness, prevent and control the impact of the pandemic by assessing the people's sentiments towards the COVID vaccines. People's sentiments are classified from not afraid to afraid divulging a total of three classes. The dataset is extracted from Twitter. The study has three main objectives 1) data collection and preprocessing 2) analyzing public sentiments, 3) evaluating the performance of Machine Learning (ML) classifiers. The results show that majority of people belong to the neutral class which indicates that they are still doubtful if they should be vaccinated or not. There is an urgent need for vaccine awareness programs to prevent COVID.