Coronavirus has emerged as challenge for the whole mankind causing illness worldwide. To eradicate the disease, global efforts are put increasing to develop its vaccine. In order to achieve the immunity against the virus, wide provision of vaccine is necessary. To make sure the distribution of vaccines, the sentiments of people for vaccines must be analyzed. Now-A-days, people share their thoughts, feelings and feedback about anything they experience on social media platforms. In this study, high performance approaches have been used for the analysis of the sentiments of people about vaccines. In this study, we have used the freely available data and applied pre-processing over it. We found out the polarity values of the tweets using TextBlob() function of Python and drew the wordclouds for positive, negative and neutral tweets. We used BERT model for understanding the people's feelings and feedback about vaccines. The model evaluation was performed using precision, recall and F measure. The BERT model achieved achieved 55 % 54 % precision, 69 % 85 % recall and 58 % 64 % F score for positive class and negative class respectively. Therefore, the use of artificial intelligence in social media analysis produce fruitful results while determining the people's attitude towards ant new trend, topic and any emergency situation. These methods helps to grow the vaccines campaigns timely by solving the people's concerns about vaccines.

Using High Performance Approaches to Covid-19 Vaccines Sentiment Analysis

Umair A.
Primo
;
Masciari E.
2022

Abstract

Coronavirus has emerged as challenge for the whole mankind causing illness worldwide. To eradicate the disease, global efforts are put increasing to develop its vaccine. In order to achieve the immunity against the virus, wide provision of vaccine is necessary. To make sure the distribution of vaccines, the sentiments of people for vaccines must be analyzed. Now-A-days, people share their thoughts, feelings and feedback about anything they experience on social media platforms. In this study, high performance approaches have been used for the analysis of the sentiments of people about vaccines. In this study, we have used the freely available data and applied pre-processing over it. We found out the polarity values of the tweets using TextBlob() function of Python and drew the wordclouds for positive, negative and neutral tweets. We used BERT model for understanding the people's feelings and feedback about vaccines. The model evaluation was performed using precision, recall and F measure. The BERT model achieved achieved 55 % 54 % precision, 69 % 85 % recall and 58 % 64 % F score for positive class and negative class respectively. Therefore, the use of artificial intelligence in social media analysis produce fruitful results while determining the people's attitude towards ant new trend, topic and any emergency situation. These methods helps to grow the vaccines campaigns timely by solving the people's concerns about vaccines.
978-1-6654-6958-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/903286
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