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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/nq.2022.20.11.NQ66336

SentimentalAnalysisOfCovid-19VaccinationTweets

Dr.SarikaZaware1,SanjanaBhosale2,RajeshreeKalburgi3,ShrutiModale4,andPratikKadam

Abstract

In March 2020, Coronavirus disease was officially announcedas a pandemic all over the world by the World Health Organization(WHO) Since then, the whole pharmaceutical world is in a state of warwith COVID-19 and has a responsibility to provide its vaccine for theentire world as soon as possible. The coronavirus outbreak has broughtunprecedented measures, which forced the authorities to make decisionsrelated to the installation of lockdown in the areas most hit by the pan-demic. Social media has been an important support for people whilepassing through this difficult period. Tweets collected, analyzed, and in-cluded in the media reports. Based on the analysis, it can be seen thatMost tweets are neutral, while the number of compatible tweets exceedsthenumberoftweetsagainsttweets.[5, 15, 17]Intermsofnews,itisconsidered that the occurrence of tweets follows the practice of events.Moreover, the proposed method can be used in a long-term monitoringcampaign that can help governments to establish appropriate commu-nication systems and to evaluate them in order to provide clear andadequate information to the general public, which can increase publicconfidenceinthevaccinecampaign.Thedatasetistrainedonamachinelearningmodeltoclassifytheopinionsofpeopleonthevaccinationpro- cess.ThealgorithmsusedareBERT,SVMandNaiveBayes.[1,7]

Keywords

BERT, Multinomial Naive bayes, SVM, Covid-19, Vaccina-tion,Sentimentalanalysis

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