Emotion Recognition using Sentiment Analysis of Covid-19 Vaccine Tweets

Abstract

With the growing availability and popularity of opinion-rich resources such as twitter, new challenges arise as people actively use information to seek out, communicate and understand the opinions of others. COVID-19 is a contagious disease that is one of the most crucial ongoing global pandemic. Vaccine development presented a hope to combat this pandemic but the negative sentiments and uncertainty to receive the vaccination, as a result of anti-vaccine movement, is a big hindrance in controlling the pandemic effectively. Another crisis has appeared in the form of unwillingness, stress and anxiety towards the COVID-19 Vaccine due to misleading and inaccurate information. COVID-19 vaccine brought a mix batch of emotions and opinions from different nations on Twitter. The aim of this thesis is to analyze the reaction of people of different countries specifically Pakistan on the basis of COVID-19 vaccine related tweets to gauge the accurate sentiment of public. Natural Language processing and Neural Networks will be used for estimating the sentiment polarity (positive, negative and neutral) and primary emotions (joy, surprise, sad, fear, disgust, anger) from the extracted tweets.