Decision Support System for Measuring the User Sentiment towards Different COVID-19 Vaccines

Abstract

It's been a long time since the (COVID-19) engulfed the entire planet, upsetting normal schedules, destroying economies, and killing millions of people all over the world. The pandemic brought the entire world together in an endeavorto discover a cure and promote inoculation. The first round of vaccines began near the end of 2020, contrary to popular belief, and various nations began the inoculation drive very quickly while others keptit together fully expecting an effective preliminary. Web-based media is blockedwith a wide scope of both positive and negative stories in the developing Covid conditions. Numerous individuals were anticipating the vaccination, while others were mindful about the side effects and the fear-inspirednotions bringing about mixed emotions. This article performs sentiment analysis, which will be utilized in a choice emotionally supportive network in discovering the viability of COVID-19 vaccines among various nations.We have trained deep long short-term memory (LSTM) models to achieve state-of-the-art accuracy in estimating sentiment polarity and emotional state from extracted tweets.Theproposed technique decides public sentiments towards COVID-19 vaccines assisting the healthcare authorities with breaking down their reaction. The results show the mentality of individuals towards various vaccine brands as for their various responses to the Covid-19 vaccines.