People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against ‘Covid19vaccine’ and ‘Coronavirusvaccine’ hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words ‘Delhi heart’, ‘Lung institute’, ‘Gift’, ‘Unite2fightcorona’, and ‘Covid-19 Vaccine’ are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards ‘Covid-19 vaccines’, gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine.
YanYShinWIPangYXet al. The first 75 days of novel coronavirus (SARS-CoV-2) outbreak: recent advances, prevention, and treatment. Int J Environ Res Public Health2020; 17(7): 2323.
2.
PaulesCIMarstonHDFauciAS. Coronavirus infections – more than just the common cold. JAMA2020; 323(8): 707–708.
MastersPSPerlmanS. Coronaviridae. In: KnipeDMHowleyPM (eds) Fields virology. 6th ed. London: Lippincott Williams & Wilkins, 2013, pp. 825–858.
6.
SuSWongGShiWet al. Epidemiology, genetic recombination, and pathogenesis of coronavirus. Trend Microbiol2016; 24(6): 490–502.
7.
FengZYuQYaoSet al. Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics. Nat Commun2020; 11(1): 4968.
8.
GandhiRTLynchJBDelR. Mild or moderate covid-19. N Eng J Med2020; 383(18): 1757–1766.
LaiCCShihTPKoWCet al. Acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents2020; 55(3): 105924.
17.
LiuYCKuoRLShihSR. COVID-19: the first documented coronavirus pandemic in history. Biomed J2020; 43(4): 328–333.
JayasuryaGGKumarSSinghBKet al. Analysis of public sentiment on COVID-19 vaccination using Twitter. IEEE Trans Comput Soc Syst2021; 9: 1101–1111.
21.
YousefinaghaniSDaraRMubarekaSet al. An analysis of COVID-19 vaccine sentiments and opinions on twitter. Int J Infect Dis2021; 108: 256–262.
22.
LoveBHimelboimIHoltonAet al. Twitter as a source of vaccination information: content drivers and what they are saying. Am J Infect Control2013; 41(6): 568–570.
CarleyKMMalikMLandwehrPMet al. Crowd sourcing disaster management: the complex nature of Twitter usage in Padang Indonesia. Saf Sci2016; 90: 48–61.
25.
McNelyB. Backchannel persistence and collaborative meaning making. In: SIGDOC’09: proceedings of the 27th ACM international conference on design of communication, Bloomington, IN, 5–7October2009, pp. 297–304. New York: ACM.
26.
BaralisECerquitelliTChiusanoSet al. Analysis of twitter data using a multiple-level clustering strategy. In: International conference on model and data engineering, September2013, pp. 13–24. Berlin; Heidelberg: Springer, https://core.ac.uk/download/pdf/18304796.pdf
MirAARathinamSGulS. Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati. Libr Hi Tech2021; 42(2): 340–356.
29.
JordanSEHovetSEFungICHet al. Using Twitter for public health surveillance from monitoring and prediction to public response. Data2018; 4(1): 6.
30.
SinnenbergLButtenheimAMPadrezKet al. Twitter as a tool for health research: a systematic review. Am J Public Health2017; 107(1): e1–e8.
31.
ShamratMFMJChakrabortySImranMMet al. Sentiment analysis on twitter Tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm. Indones J Electr Eng Comput Sci2021; 23(1): 463–470.
32.
TomenyTSVargoCJEl-ToukhyS. Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15. Soc Sci Med2017; 191: 168–175.
33.
VelascoEAghenezaTDeneckeKet al. Social media and internet-based data in global systems for public health surveillance: a systematic review. Milbank Quart2014; 92(1): 7–33.
34.
YousefinaghaniSDaraRPoljakZet al. The assessment of Twitter’s potential for outbreak detection: avian influenza case study. Sci Rep2019; 9(1): 1–17.
35.
ArdiantoRRivanieTAlkhalifiYet al. Sentiment analysis on E-sports for education curriculum using Naive Bayes and support vector machine. Jurnal Ilmu Komputer Dan Informasi2020; 13(2): 109–122.
ZhaoSZhongLWickramasuriyaJet al. Analyzing twitter for social TV: sentiment extraction for sports. Proc Int Work Future Telev2011; 2: 11–18.
38.
YuYWangX. World Cup 2014 in the Twitter World: a big data analysis of sentiments in US sports fans’ Tweets. Comput Hum Behav2015; 48: 392–400.
39.
SchumakerRPJarmoszkoATLabedzCS. Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decis Support Syst2016; 88: 76–84.
40.
WunderlichFMemmertD. Innovative approaches in sports science – Lexicon-based sentiment analysis as a tool to analyze sports-related Twitter communication. Appl Sci2020; 10(2): 431.
41.
HegdeSUZaibaASNagarajuY. Hybrid CNN-LSTM model with GloVe word vector for sentiment analysis on football specific Tweets. In: ICAECT, Bhilai, India, 19–20February2021, pp. 1–8. New York: IEEE.
42.
GuptaVSKohliS. Twitter sentiment analysis in healthcare using Hadoop and R. In: 2016 3rd international conference on computing for sustainable global development (Indiacom), New Delhi, India,16–18March2016, pp. 3766–3772. New York: IEEE.
43.
GohilSVuikSDarziA. Sentiment analysis of health care Tweets: review of the methods used. JMIR Pub Health Surveill2018; 4(2): e5789.
44.
ClarkEMJamesTJonesCAet al. A sentiment analysis of breast cancer treatment experiences and healthcare perceptions across twitter. Arxiv Preprint, 2018, https://arxiv.org/abs/1805.09959
45.
BeigiGHuXMaciejewskiRet al. An overview of sentiment analysis in social media and its applications in disaster relief. Sent Anal Ontol Eng2016; 2016: 313–340.
46.
VeraVDGSuárezLMMLoperaICP. Sentiment analysis on post conflict in Colombia: a text mining approach. Asian J Appl Sci2018; 6(2): 885.
47.
PengnateSFLehmbergDGTangpongC. Top management’s communication in economic crisis and the firm’s subsequent performance: sentiment analysis approach. Corporate Commun2020; 25(2): 187–205.
48.
XiongJHswenYNaslundJA. Digital surveillance for monitoring environmental health threats: a case study capturing public opinion from Twitter about the 2019 Chennai water crisis. Int J Environ Res Public Health2020; 17(14): 5077.
49.
BashirSBanoSShuebSet al. Twitter chirps for Syrian people: sentiment analysis of Tweets related to Syria Chemical attack. Int J Disaster Risk Reduct2021; 62: 102397.
50.
TianXHeWWangFK. Applying sentiment analytics to examine social media crises: a case study of United Airline’s crisis in 2017. Data Technol Appl2021; 56(1): 1–23.
51.
MarcecRLikicR. Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgrad Med J2021; 98: 140685.
52.
LyuJCLeHELuliGK. COVID-19 vaccine–related discussion on Twitter: topic modeling and sentiment analysis. J Medical Internet Res2021; 23(6): e24435.
53.
SattarNSArifuzzamanS. COVID-19 Vaccination awareness and aftermath: public sentiment analysis on Twitter data and vaccinated population prediction in the USA. Appl Sci2021; 11(13): 6128.
54.
AnsariMKhanN. Worldwide COVID-19 vaccines sentiment analysis through Twitter content. Electron J General Med2021; 18(6): 11316.
55.
LiuSLiuJ. Public attitudes toward COVID-19 vaccines on English-language Twitter: a sentiment analysis. Vaccine2021; 39: 5499–5505.
56.
HuTWangSLuoWet al. Revealing public opinion towards COVID-19 vaccines with Twitter data in the United States: spatiotemporal perspective. J Med Internet Res2021; 23(9): e30854.
NuzhathTTasnimSSanjwalRKet al. COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: a content analysis of Twitter data, 2020, https://osf.io/preprints/socarxiv/vc9jb/
59.
GriffithJMaraniHMonkmanH. COVID-19 vaccine hesitancy in Canada: content analysis of Tweets using the theoretical domains framework. J Med Int Res2021; 23(4): e26874.
60.
BonnevieEGallegosJAGoldbargJet al. Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. J Commun Healthc2021; 14(1): 12–19.
61.
ThelwallMKoushaKThelwallS. Covid-19 vaccine hesitancy on English-language Twitter. Prof De La Inf (EPI)2021; 30(2): 3145.
62.
JemielniakDKrempovychY. An analysis of AstraZeneca COVID-19 vaccine misinformation and fear mongering on Twitter. Public Health2021; 200: 4–6.
63.
HayawiKShahriarSSerhaniMAet al. ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection. Public Health2022; 203: 23–30.
64.
SharmaKZhangYLiuY. COVID-19 vaccines: characterizing misinformation campaigns and vaccine hesitancy on twitter. Arxiv Preprint2021; 2021: 2106.08423.
HussainATahirAHussainZet al. Artificial intelligence–enabled analysis of public attitudes on Facebook and Twitter toward COVID-19 vaccines in the United Kingdom and the United States: observational study. J Med Internet Res2021; 23(4): e26627.
KwokSWHVaddeSKWangG. Tweet topics and sentiments relating to COVID-19 vaccination among Australian Twitter users: machine learning analysis. J Med Internet Res2021; 23(5): e26953.
72.
GulatiS. Decoding the global trend of ‘vaccine tourism’ through public sentiments and emotions: does it get a nod on Twitter. Glob Knowl Mem Commun. Epub ahead of print 9September2021. DOI: 10.1108/GKMC-06-2021-0106.
73.
BontaVJanardhanNKN. A comprehensive study on lexicon based approaches for sentiment analysis. Asian J Comput Sci Technol2019; 8(S2): 1–6.
74.
HungMLaurenEHonESet al. Social network analysis of COVID-19 sentiments: application of artificial intelligence. J Medical Internet Res2020; 22(8): e22590.
WaltmanLvan EckNJNoyonsECM. A unified approach to mapping and clustering of bibliometric networks. J Informetr2010; 4(4): 629–635.
77.
YoonYSOhHWParkK. Mining twitter to identify customers’ requirements and shoe market segmentation. In: International conference on information and communication technology convergence (ICTC), Jeju, South Korea, 17–19October2018, pp. 1194–1199. New York: IEEE.
78.
NoorSGuoYShahSHHet al. Analysis of public reactions to the novel Coronavirus (COVID-19) outbreak on Twitter. Kybernetes2020; 50(5): 1633–1653.
79.
EckNJVWaltmanL. Visualizing bibliometric networks. In: DingTRousseauRWaltmanD (eds) Measuring scholarly impact: methods and practice. Cham: Springer, 2014, pp. 285–320.
80.
Van EckNJWaltmanL. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics2017; 111(2): 1053–1070.
81.
BrunsonEK. The impact of social networks on parents’ vaccination decisions. Pediatrics2013; 131(5): e1397–e1404.
MonseliseMChangCHFerreiraGet al. Topics and sentiments of public concerns regarding COVID-19 vaccines: social media trend analysis. J Medical Internet Res2021; 23(10): e30765.
84.
NezhadZBDeihimiMA. Twitter sentiment analysis from Iran about COVID 19 vaccine. Diabet Metabol Syndr2022; 16(1): 102367.
85.
RodriguezTDDominguezLY. Social media in crisis communication: Germanwings Flight 4U9525. In: FreireFRúas AraújoXMartínez FernándezVet al. (eds) Media and metamedia management. Advances in intelligent systems and computing, vol. 503. Cham: Springer, 2017, pp. 243–249.
