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dc.contributor.author
Kapoteli, Evridiki
en
dc.date.accessioned
2022-06-16T10:07:45Z
dc.date.available
2022-06-16T10:07:45Z
dc.date.issued
2022-06-16
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29981
dc.rights
Default License
dc.subject
Social media
en
dc.subject
Sentiment analysis
el
dc.subject
Covid 19
el
dc.subject
Vaccines
el
dc.title
Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case studies in English and Greek language
en
heal.type
masterThesis
en_US
heal.dateAvailable
2022-05-16
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2022-01-07
heal.abstract
SARS-CoV-2 and its mutations are rapidly spreading around the world threatening human population with millions of infections and deaths. Vaccines are the only available weapon at hand to mitigate the spread. As a result, the development of efficient systems to understand and supervise the information dissemination as well as the sentiment evolution toward vaccines is critical. The goal of this research was to build and apply a supervised machine learning approach to monitor the dynamics of public opinion on COVID-19 vaccines using Twitter data. 1,394,535 and 61,077 tweets in English and Greek about COVID-19 vaccines, respectively, were collected, classified based on sentiment polarity, and analyzed over time to gain insights into sentiment trends. The findings reveal that overall negative, neutral, and positive sentiments were at 36.5%, 39.9% and 23.6% in the English language dataset, respectively, whereas overall negative and non-negative sentiments were at 60.1% and 39.9% in the Greek language dataset. Policy makers and health experts should take into consideration social media sentiment analysis alongside other ways of evaluating public sentiment. Social media users are actively seeking and sharing information about all pandemic-related topics, allowing governments to use social media not only to better inform the public with accurate and reliable news, but also alleviate disease-specific concerns, minimize the distribution of fake news, and develop effective crisis management strategies.
el
heal.advisorName
Tjortjis, Christos
el
heal.committeeMemberName
Tzafilkou, Katerina
en
heal.committeeMemberName
Karapiperis, Dimitrios
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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