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Mamman, Jacob (2023) Sentiment Analysis on Covid-19 Booster shots Vaccinations. Masters thesis, Dublin, National College of Ireland.
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Abstract
The rapid growth of social networks and the ease of internet access have accelerated the spread of incorrect information and rumours on social media platforms. By putting people’s mental and physical well-being in jeopardy, this misinformation has made the COVID-19 epidemic much more severe. Whether or whether booster shots of the COVID-19 vaccine will be required is also uncertain at this time. Consequently, in order to clear up this misunderstanding and minimize the widespread anxiety, the sentiment analysis of the sentiment analysis on the covid-19 booster shots vaccination was conducted using a dataset from the Twitter online platform. The study analyzes the sentiment of online discussions around the covid-19 booster shot vaccines using TEXTBLOB as a baseline model and the BERT and RoBERTa algorithms to predict those sentiments and evaluate their performance. The 46,398-item data set utilized was obtained through the Twitter API. The BERT algorithm had better results than the ROBERTA with 0.1 margins, with an F1 accuracy score of 100%.
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