{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:43:45Z","timestamp":1779381825624,"version":"3.53.1"},"reference-count":40,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,9,22]]},"abstract":"<jats:p>This work presents the analysis of significant sentiments and attitudes of people towards the COVID-19 vaccination. The tweeter messages related to the COVID-19 vaccine is used for sentiment evaluation in this work. The proposed work consists of two steps: (i) natural processing language (NLP) and (ii) classification. The NLP is utilized for text pre-processing, tokenization, data labelling, and feature extraction. Further, a stack-based ensemble machine learning model is used to classify sentiments as positive, negative, or neutral. The stack ensemble machine learning model includes seven heterogeneous machine learning techniques namely, Naive Bayes, Logistic regression, Decision Tree, Random Forest, AdaBoost Classifier, Gradient Boosting, and extreme Gradient Boosting (XGB). The highest classification accuracy of 97.2%, 88.34%, 88.22%, 85.23%, 86.30%, 87.54%, 86.63%, and 88.78% is achieved by ensemble machine learning model, Logistic regression, AdaBoost, Decision Tree, Naive Bayes, Random Forest, Gradient Boosting, and XGB Classifier, respectively.<\/jats:p>","DOI":"10.3233\/jifs-220279","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T13:24:09Z","timestamp":1660915449000},"page":"6307-6319","source":"Crossref","is-referenced-by-count":12,"title":["Multilayer hybrid ensemble machine learning model for analysis of Covid-19 vaccine sentiments"],"prefix":"10.1177","volume":"43","author":[{"given":"Vipin","family":"Jain","sequence":"first","affiliation":[{"name":"VIT University, Bhopal, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanchan Lata","family":"Kashyap","sequence":"additional","affiliation":[{"name":"VIT University, Bhopal, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-220279_ref1","doi-asserted-by":"crossref","unstructured":"Alam K.N. , Khan M.S. , Dhruba A.R. , Khan M.M. , Al-Amri J.F. , Masud M. and Rawashdeh M. , Deep learningbased sentiment analysis of covid-19 vaccination responses from twitter data, Computational and Mathematical Methods in Medicine, 2021.","DOI":"10.1155\/2021\/4321131"},{"key":"10.3233\/JIFS-220279_ref2","doi-asserted-by":"crossref","unstructured":"Aygun I. , Kaya B. and Kaya M. , Aspect based twitter sentiment analysis on vaccination and vaccine types in covid-19 pandemic with deep learning, IEEE Journal of Biomedical and Health Informatics, (2021).","DOI":"10.1109\/JBHI.2021.3133103"},{"key":"10.3233\/JIFS-220279_ref3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3897\/jucs.2020.004","article-title":"Detecting epidemic diseases using sentiment analysis of arabic tweets","volume":"26","author":"Baker","year":"2020","journal-title":"J Univers Comput Sci"},{"key":"10.3233\/JIFS-220279_ref4","doi-asserted-by":"crossref","first-page":"S326","DOI":"10.2105\/AJPH.2020.305901","article-title":"Content themes and influential voices within vaccine opposition on twitter","volume":"110","author":"Bonnevie","year":"2020","journal-title":"American Journal of Public Health"},{"key":"10.3233\/JIFS-220279_ref5","unstructured":"Brajawidagda U. and Chatfield A.T. , Twitter tsunami early warning network: A social network analysis of twitter information flows, (2012)."},{"key":"10.3233\/JIFS-220279_ref6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-220279_ref7","unstructured":"Buntain C. , Golbeck J. , Liu B. and LaFree G. , Evaluating public response to the boston marathon bombing and other acts of terrorism through twitter, in: Proceedings of the international AAAI conference on web and social media, (2016)."},{"key":"10.3233\/JIFS-220279_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-319-55394-8","volume-title":"A practical guide to sentiment analysis","author":"Cambria","year":"2017"},{"key":"10.3233\/JIFS-220279_ref9","doi-asserted-by":"crossref","first-page":"106754","DOI":"10.1016\/j.asoc.2020.106754","article-title":"Sentiment analysis of covid-19 tweets by deep learning classifiers\u2013 a study to show how popularity is affecting accuracy in social media","volume":"97","author":"Chakraborty","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.3233\/JIFS-220279_ref10","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0304-3975(87)90130-7","article-title":"Discrete decision theory: Manipulations","volume":"54","author":"Cockett","year":"1987","journal-title":"Theoretical Computer Science"},{"key":"10.3233\/JIFS-220279_ref11","unstructured":"R. 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