{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T09:12:50Z","timestamp":1791969170449,"version":"4.3.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people\u2019s opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.<\/jats:p>","DOI":"10.1007\/s11280-022-01029-y","type":"journal-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T18:02:46Z","timestamp":1645812166000},"page":"1067-1083","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":110,"title":["Sentiment analysis and topic modeling for COVID-19 vaccine discussions"],"prefix":"10.1007","volume":"25","author":[{"given":"Hui","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuiqiao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-330X","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"issue":"2","key":"1029_CR1","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1109\/TCSS.2018.2831694","volume":"5","author":"N Alduaiji","year":"2018","unstructured":"Alduaiji, N., Datta, A., Li, J.: Influence propagation model for clique-based community detection in social networks. IEEE Transactions on Computational Social Systems 5(2), 563\u2013575 (2018)","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"1029_CR2","doi-asserted-by":"crossref","unstructured":"Bang, Y., Ishii, E., Cahyawijaya, S., Ji, Z., Fung, P.: Model generalization on covid-19 fake news detection. arXiv:2101.03841, (2021)","DOI":"10.1007\/978-3-030-73696-5_13"},{"issue":"Jan","key":"1029_CR3","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3(Jan), 993\u20131022 (2003)","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"1029_CR4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1080\/17538068.2020.1858222","volume":"14","author":"E Bonnevie","year":"2021","unstructured":"Bonnevie, E., Gallegos-Jeffrey, A., Goldbarg, J., Byrd, B., Smyser, J.: Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. Journal of communication in healthcare 14(1), 12\u201319 (2021)","journal-title":"Journal of communication in healthcare"},{"issue":"1","key":"1029_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-019-0084-2","volume":"7","author":"J Du","year":"2019","unstructured":"Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Information Science and Systems 7(1), 1\u20137 (2019)","journal-title":"Health Information Science and Systems"},{"key":"1029_CR6","doi-asserted-by":"crossref","unstructured":"Dubey, A.D.: Twitter sentiment analysis during covid-19 outbreak. Available at SSRN 3572023 (2020)","DOI":"10.2139\/ssrn.3572023"},{"issue":"12","key":"1029_CR7","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1038\/s41562-020-00994-6","volume":"4","author":"R Gallotti","year":"2020","unstructured":"Gallotti, R., Valle, F., Castaldo, N., Sacco, P., Domenico, M.D.: Assessing the risks of \u2018infodemics\u2019 in response to covid-19 epidemics. Nature Human Behaviour 4(12), 1285\u20131293 (2020)","journal-title":"Nature Human Behaviour"},{"key":"1029_CR8","doi-asserted-by":"crossref","unstructured":"Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8 (2014)","DOI":"10.1609\/icwsm.v8i1.14550"},{"issue":"6","key":"1029_CR9","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1007\/s11280-018-0639-1","volume":"22","author":"H Jiang","year":"2019","unstructured":"Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: Sentence level topic models for associated topics extraction. World Wide Web 22(6), 2545\u20132560 (2019)","journal-title":"World Wide Web"},{"issue":"5","key":"1029_CR10","doi-asserted-by":"publisher","first-page":"e26953","DOI":"10.2196\/26953","volume":"23","author":"SWH Kwok","year":"2021","unstructured":"Kwok, S.W.H., Vadde, S.K., Wang, G.: Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: Machine learning analysis. Journal of Medical Internet Research 23(5), e26953 (2021)","journal-title":"Journal of Medical Internet Research"},{"key":"1029_CR11","doi-asserted-by":"crossref","unstructured":"Li, J., Cai, T., Deng, K., Wang, X., Sellis, T., Xia, F.: Community-diversified influence maximization in social networks. Information Systems, pp 101522 (2020)","DOI":"10.1016\/j.is.2020.101522"},{"key":"1029_CR12","unstructured":"Li, X., Zhou, M., Wu, J., Yuan, A., Wu, F., Li, J.: Analyzing covid-19 on online social media: trends, sentiments and emotions. arXiv:2005.14464, (2020)"},{"issue":"6","key":"1029_CR13","doi-asserted-by":"publisher","first-page":"e24435","DOI":"10.2196\/24435","volume":"23","author":"JC Lyu","year":"2021","unstructured":"Lyu, J.C., Han, E.L., Luli, G.K.: Covid-19 vaccine-related discussion on twitter: Topic modeling and sentiment analysis. Journal of Medical Internet Research 23(6), e24435 (2021)","journal-title":"Journal of Medical Internet Research"},{"issue":"1","key":"1029_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00125-5","volume":"8","author":"R Sarki","year":"2020","unstructured":"Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Information Science and Systems 8(1), 1\u20139 (2020)","journal-title":"Health Information Science and Systems"},{"key":"1029_CR15","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ins.2021.08.100","volume":"580","author":"X Song","year":"2021","unstructured":"Song, X., Li, J., Tang, Y., Zhao, T., Chen, Y., Guan, Z.: Jkt: A joint graph convolutional network based deep knowledge tracing. Information Sciences 580, 510\u2013523 (2021)","journal-title":"Information Sciences"},{"issue":"2","key":"1029_CR16","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/bdcc4020014","volume":"4","author":"M Stella","year":"2020","unstructured":"Stella, M., Restocchi, V., De Deyne, S.: # lockdown: Network-enhanced emotional profiling in the time of covid-19. Big Data and Cognitive Computing 4(2), 14 (2020)","journal-title":"Big Data and Cognitive Computing"},{"key":"1029_CR17","doi-asserted-by":"crossref","unstructured":"Thelwall, M., Kousha, K., Thelwall, S.: Covid-19 vaccine hesitancy on english-language twitter. Profesional de la informaci\u00f3n (EPI), 30(2) (2021)","DOI":"10.3145\/epi.2021.mar.12"},{"issue":"6","key":"1029_CR18","doi-asserted-by":"publisher","first-page":"2883","DOI":"10.1007\/s11280-018-0618-6","volume":"22","author":"Q Tian","year":"2019","unstructured":"Tian, Q., Li, J., Chen, L., Deng, K., Li, R.-H., Reynolds, M., Liu, C.: Evidence-driven dubious decision making in online shopping. World Wide Web 22(6), 2883\u20132899 (2019)","journal-title":"World Wide Web"},{"issue":"4","key":"1029_CR19","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1007\/s11280-019-00748-z","volume":"23","author":"X Wang","year":"2020","unstructured":"Wang, X., Deng, K., Li, J., Yu, J.X., Jensen, C.S., Yang, X.: Efficient targeted influence minimization in big social networks. World Wide Web 23(4), 2323\u20132340 (2020)","journal-title":"World Wide Web"},{"key":"1029_CR20","doi-asserted-by":"crossref","unstructured":"Yang, S., Huang, G., Xiang, Y., Zhou, X., Chi, C.-H.: Modeling user preferences on spatiotemporal topics for point-of-interest recommendation. In: 2017 IEEE International Conference on Services Computing (SCC), pp. 204\u2013211. IEEE (2017)","DOI":"10.1109\/SCC.2017.33"},{"key":"1029_CR21","doi-asserted-by":"publisher","first-page":"92037","DOI":"10.1109\/ACCESS.2019.2927345","volume":"7","author":"S Yang","year":"2019","unstructured":"Yang, S., Huang, G., Cai, B.: Discovering topic representative terms for short text clustering. IEEE Access 7, 92037\u201392047 (2019)","journal-title":"IEEE Access"},{"key":"1029_CR22","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/OJCS.2020.3028573","volume":"1","author":"S Yang","year":"2020","unstructured":"Yang, S., Jiang, J., Pal, A., Yu, K., Chen, F., Yu, S.: Analysis and insights for myths circulating on twitter during the covid-19 pandemic. IEEE Open Journal of the Computer Society 1, 209\u2013219 (2020)","journal-title":"IEEE Open Journal of the Computer Society"},{"key":"1029_CR23","doi-asserted-by":"crossref","unstructured":"Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web, pp 1\u201323 (2021)","DOI":"10.1007\/s11280-021-00909-z"},{"key":"1029_CR24","doi-asserted-by":"crossref","unstructured":"Yin, H., Yang, S., Li, J.: Detecting topic and sentiment dynamics due to covid-19 pandemic using social media. In: International Conference on Advanced Data Mining and Applications, pp 610\u2013623. Springer (2020)","DOI":"10.1007\/978-3-030-65390-3_46"},{"key":"1029_CR25","unstructured":"Yin, H., Yang, S., Song, X., Liu, W., Li, J.: Deep fusion of multimodal features for social media retweet time prediction. World Wide Web, pp 1\u201318 (020)"},{"issue":"5","key":"1029_CR26","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.1007\/s11280-020-00813-y","volume":"23","author":"F Zhang","year":"2020","unstructured":"Zhang, F., Wang, Y., Liu, S., Wang, H.: Decision-based evasion attacks on tree ensemble classifiers. World Wide Web 23(5), 2957\u20132977 (2020)","journal-title":"World Wide Web"},{"key":"1029_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, J., Zogan, H., Yang, S., Jameel, S., Xu, G., Chen, F.: Detecting community depression dynamics due to covid-19 pandemic in australia. IEEE Transactions on Computational Social Systems (2021)","DOI":"10.1109\/TCSS.2020.3047604"},{"issue":"3","key":"1029_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00596-7","volume":"2","author":"J Zhou","year":"2021","unstructured":"Zhou, J., Yang, S., Xiao, C., Chen, F.: Examination of community sentiment dynamics due to covid-19 pandemic: A case study from a state in australia. SN Computer Science 2(3), 1\u201311 (2021)","journal-title":"SN Computer Science"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01029-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-022-01029-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01029-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T23:02:57Z","timestamp":1674860577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-022-01029-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["1029"],"URL":"https:\/\/doi.org\/10.1007\/s11280-022-01029-y","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,25]]},"assertion":[{"value":"9 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest. The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}