Sentiment analysis system for COVID-19 vaccinations using data of Twitter

Abstract

COVID-19 vaccination topic has been a hot topic of discussions on social media platforms wondering its effectiveness against the SARS-COV-2 virus. Twitter is one of the social media platforms that people widely lunched to express and share their thoughts about different issues touching their daily life. Though many studies have been undertaken for COVID-19 vaccine sentiment analysis, they are still limited and need to be updated constantly. This paper conducts a system for COVID-19 vaccine sentiment analysis based on data extracted from Twitter platform for the time interval from 1st of January till the 3rd of Sep. 2021, and by using deep learning techniques. The introduced system proposes to develop a model architecture based on a deep bidirectional long short-term memory (LSTM) neural network, to analyze tweets data in the form of positive, neutral, and negative. As a result, the overall accuracy of the developed model based on validation data is 74.92%. The obtained outcomes from the sentiment analysis system on collected tweets-data of COVID-19 vaccine revealed that neutral is the prominent sentiment with a rate of 69.5%, and negative sentiment has less rate of tweets reached 20.75% while the positive sentiment has a lesser rate of tweets reached of 9.67%.