Ramalingam, Sourav (2022) Sentiment Analysis on Covid-19 Vaccination Reviews Using BERT and Comparative Study with LSTM, Vader, and Text blob Models. Masters thesis, Dublin, National College of Ireland.
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Abstract
The pandemic of 2019 is known to everyone, as it significantly impacted everyone's life. The pandemic took a toll on the economic as well as the mental situation of the people. In such a situation, it became difficult for the government in meeting everyone's needs. Social media paved the way for the government to analyse the situation based on the people's opinion and their mindset towards the pandemic. Once the vaccination was available in the market for public usage, it was also noticed that people had hesitations in getting vaccinated as many rumours were spreading around by word of mouth and through the reviews that were posted on social media platforms. As per the research, the Covid-19 vaccination proved to be effective in fighting against the virus. Anyways, the public had different opinions about the effects of vaccination. So, it has also become important for the government to analyse the people’s opinions about the different vaccination, which could be done using the sentiment analysis approach applied to the data generated from social media platforms.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Hafeez, Taimur UNSPECIFIED |
Uncontrolled Keywords: | BERT - Bidirectional encoder representation from transformers; LSTM - Long short-term memory; NLP - Natural language processing; Pre-trained Sentiment Analysis Models; Opinion Mining |
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing |
Divisions: | School of Computing > Master of Science in Data Analytics |
Depositing User: | Tamara Malone |
Date Deposited: | 24 May 2023 18:32 |
Last Modified: | 24 May 2023 18:32 |
URI: | https://norma.ncirl.ie/id/eprint/6639 |
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