Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning
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Date
2022
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Abstract
Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important factor hampering vaccination activities. In this study, aspect-base sentiment analysis was conducted for USA, U.K., Canada, Turkey, France, Germany, Spain and Italy showing the approach of twitter users to vaccination and vaccine types during the COVID-19 period. Within the scope of this study, two datasets in English and Turkish were prepared with 928,402 different vaccine-focused tweets collected by country. In the classification of tweets, 4 different aspects (policy, health, media and other) and 4 different BERT models (mBERT-base, BioBERT, ClinicalBERT and BERTurk) were used. 6 different COVID-19 vaccines with the highest frequency among the datasets were selected and sentiment analysis was made by using Twitter posts regarding these vaccines. To the best of our knowledge, this paper is the first attempt to understand people's views about vaccination and types of vaccines. With the experiments conducted, the results of the views of the people on vaccination and vaccine types were presented according to the countries. The success of the method proposed in this study in the F1 Score was between 84% and 88% in datasets divided by country, while the total accuracy value was 87%. © 2013 IEEE.
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Keywords
COVID-19 , COVID-19 Vaccines , Deep Learning , Humans , Pandemics , SARS-CoV-2 , Sentiment Analysis , Social Media , Vaccination , Binary alloys , Blogs , Classification (of information) , Data mining , Deep learning , Potassium alloys , Sentiment analysis , Uranium alloys , Vaccines , ad26.cov2.s vaccine , coronavac , elasomeran , nvx-cov2373 vaccine , SARS-CoV-2 vaccine , tozinameran , vaxzevria , BERT , Biomedical measurements , COVID-19 , COVID-19 vaccine , Deep learning , Pandemic , Sentiment analysis , Social networking (online) , Text-mining , Article , BERT model , Canada , coronavirus disease 2019 , data mining , deep learning , F1 score , France , geographic distribution , Germany , human , Italy , machine learning , mathematical model , mathematical parameters , measurement accuracy , name entity recognition , sentiment analysis , social media , Spain , Turkey (republic) , United Kingdom , vaccination , Wart virus , drug therapy , epidemiology , pandemic , prevention and control , vaccination , Social networking (online)