Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques

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As a result of the COVID-19 epidemic, many individuals are experiencing extreme worry, dread, and other difficult emotions. Since coronavirus immunizations were first introduced, people's reactions have gotten more nuanced and varied. In this study, we will use deep learning methods to decode their emotions. Twitter provides a glimpse into what is popular and what is on people's minds at any given time, and social media is presently the finest means to convey sentiments and emotions. Our goal while conducting this study was to have a better grasp of how different groups of individuals feel about vaccinations. The research period for this research's tweet was from December 21st to July 21st. Of the most talked-about vaccines that have recently been available in various regions of the world were the subject of several tweets. The term Valence Aware Sentiment Dictionary An NLP program called Believed (VADER) was used to examine people's sentiments on certain vaccines. We were better able to see the big picture after categorizing the collected attitudes into positive (33.96 percent), negative (17.55 percent), and neutral (48.49 percent) camps. We also included into our study an examination of the tweets' chronology, given that attitudes changed over time. The performance of the forecasting models was evaluated using an RNN-oriented design that included bidirectional LSTM (Bi-LSTM) as well as long short-term memory (LSTM); LSTM attained an accuracy of 90.59% as well as BiLSTM of 90.83%. Additional performance metrics, such as Precision, F1-score, as well as a matrix of confusion, were used to confirm our hypotheses as well as outcomes. The findings of this research provide credence to efforts to eradicate coronavirus across the globe by expanding our knowledge of public opinion on COVID-19 vaccines.