Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning

Simple item page
dc.contributor.authorAygun I.
dc.contributor.authorKaya B.
dc.contributor.authorKaya M.
dc.date.accessioned2024-07-22T08:04:27Z
dc.date.available2024-07-22T08:04:27Z
dc.date.issued2022
dc.description.abstractDue 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.
dc.identifier.DOI-ID10.1109/JBHI.2021.3133103
dc.identifier.issn21682194
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12701
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectCOVID-19
dc.subjectCOVID-19 Vaccines
dc.subjectDeep Learning
dc.subjectHumans
dc.subjectPandemics
dc.subjectSARS-CoV-2
dc.subjectSentiment Analysis
dc.subjectSocial Media
dc.subjectVaccination
dc.subjectBinary alloys
dc.subjectBlogs
dc.subjectClassification (of information)
dc.subjectData mining
dc.subjectDeep learning
dc.subjectPotassium alloys
dc.subjectSentiment analysis
dc.subjectUranium alloys
dc.subjectVaccines
dc.subjectad26.cov2.s vaccine
dc.subjectcoronavac
dc.subjectelasomeran
dc.subjectnvx-cov2373 vaccine
dc.subjectSARS-CoV-2 vaccine
dc.subjecttozinameran
dc.subjectvaxzevria
dc.subjectBERT
dc.subjectBiomedical measurements
dc.subjectCOVID-19
dc.subjectCOVID-19 vaccine
dc.subjectDeep learning
dc.subjectPandemic
dc.subjectSentiment analysis
dc.subjectSocial networking (online)
dc.subjectText-mining
dc.subjectArticle
dc.subjectBERT model
dc.subjectCanada
dc.subjectcoronavirus disease 2019
dc.subjectdata mining
dc.subjectdeep learning
dc.subjectF1 score
dc.subjectFrance
dc.subjectgeographic distribution
dc.subjectGermany
dc.subjecthuman
dc.subjectItaly
dc.subjectmachine learning
dc.subjectmathematical model
dc.subjectmathematical parameters
dc.subjectmeasurement accuracy
dc.subjectname entity recognition
dc.subjectsentiment analysis
dc.subjectsocial media
dc.subjectSpain
dc.subjectTurkey (republic)
dc.subjectUnited Kingdom
dc.subjectvaccination
dc.subjectWart virus
dc.subjectdrug therapy
dc.subjectepidemiology
dc.subjectpandemic
dc.subjectprevention and control
dc.subjectvaccination
dc.subjectSocial networking (online)
dc.titleAspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning
dc.typeArticle

Files

Collections

Scopus Koleksiyonu