Abstract
The SARS-CoV-2 virus causes COVID-19, which is a painful and infectious disease. The virus spreads rapidly from an infected person’s lips or nose. It is said that vaccines are an excellent option for staying safe and they allow us to stand shoulder to shoulder in schools, neighbourhoods, and places of worship. As we know social media is like a powerhouse of information. In this medium, individuals express their thoughts about vaccination by posting their vaccine-related queries and experiences. This proposed system has analyzed a tweet dataset concerning Pfizer Vaccine to explore the evolution of public opinions on COVID-19 vaccination before they are taking the vaccine. It aims to see how people reacted to the product by looking at their tweets during the product’s early stages and understanding consumers’ feelings in a better way. In this model, multiple pre-processing and feature extraction techniques such as TF-IDF and N-gram have been used. Various machine learning algorithms with several combinations of ideal hyperparameters have been used in each stage to analyze many elements as possible. It has been observed that the most outstanding results are achieved in gradient boosting using TF-IDF and word tokenization. Also, the proposed approach is used to broader surveillance vaccination campaigns, help governments develop, and evaluate appropriate communication channels for providing relevant information on these. It is potential to increase public confidence.
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Haque, P., Fariha, R.J., Nishat, I.Y., Uddin, M.N. (2023). Sentiment Analysis of Tweets on Covid Vaccine (Pfizer): A Boosting-Based Machine Learning Solution. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_33
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