Analysis of Public Agenda during Covid-19 Pandemics Based on Turkish and English Tweets Using Nonnegative Matrix Factorization and Hypothesis Testing

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dc.contributor.authorEkinci, Yeliz
dc.date.accessioned2022-11-08T08:27:29Z
dc.date.available2022-11-08T08:27:29Z
dc.date.issued2022
dc.description.abstractAbstract: In this study, Turkish and English tweets through Twitter Application Program Interface (API) between 1-31 January 2021 are analyzed with respect to Covid-19. The collected tweets are preprocessed, labeled with the Vader Sentiment library, and then analyzed by topic modeling with Nonnegative Matrix Factorization. The analysis show that the most frequently mentioned word is “vaccine/aşı” after “Covid”. The topics modelled in the study are grouped into themes and the themes are seen to be similar in both languages, which means that the Turkish and world agenda are not very different in terms of themes in pandemics. Moreover, hypothesis tests are conducted to understand whether language and time period are related to sentiment class. The results show that the Turkish people are more neutral about the Covid-19 issue than other people in the world during the given period of time. Moreover, independent of the language, there are more negative and neutral tweets in the first half of January 2021, whereas there are more positive tweets in the second half of the month. To the best of our knowledge, this is the first study to analyze Covid-19 related tweets in two languages to compare the local and global agendas using topic modeling, sentiment analysis, and hypothesis testing methods. © 2022 Kauno Technologijos Universitetas. All rights reserved.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.5755/j02.eie.31196en_US
dc.identifier.issn1392-1215
dc.identifier.scopus2-s2.0-85138500473en_US
dc.identifier.urihttps://hdl.handle.net/11411/4653
dc.identifier.urihttps://doi.org/10.5755/j02.eie.31196
dc.identifier.wosWOS:000966368600008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue4en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors3en_US
dc.pages65-73en_US
dc.publisherKauno Technologijos Universitetasen_US
dc.relation.ispartofElektronika ir Elektrotechnikaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChi-square analysisen_US
dc.subjectCOVID-19en_US
dc.subjectSentiment analysisen_US
dc.subjectTopic modelingen_US
dc.subjectTwitteren_US
dc.titleAnalysis of Public Agenda during Covid-19 Pandemics Based on Turkish and English Tweets Using Nonnegative Matrix Factorization and Hypothesis Testingen_US
dc.typeArticleen_US
dc.volume28en_US

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