{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T09:48:20Z","timestamp":1771840100045,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Respiratory viruses known as coronaviruses infect people and cause death. The multiple crown-like spikes on the virus\u2019s surface give them the name \u201ccorona\u201d. The pandemic has resulted in a global health crisis and it is expected that every year we will have to fight against different COVID-19 variants. In this critical situation, the existence of COVID-19 vaccinations provides hope for mankind. Despite severe vaccination campaigns and recommendations from health experts and the government, people have perceptions regarding vaccination risks and share their views and experiences on social media platforms. Social attitudes to these types of vaccinations are influenced by their positive and negative effects. The analysis of such opinions can help to determine social trends and formulate policies to increase vaccination acceptance. This study presents a methodology for sentiment analysis of the global perceptions and perspectives related to COVID-19 vaccinations. The research is performed on five vaccinations that include Sinopharm, Pfizer, Moderna, AstraZeneca, and Sinovac on the Twitter platform extracted using Twitter crawling. To effectively perform this research, tweets datasets are categorized into three groups, i.e., positive, negative and natural. For sentiment classification, different machine learning classifiers are used such as Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). It should be noted that the Decision tree classifier achieves the highest classification performance in all datasets as compared to the other machine learning algorithms. For COVID-19 Vaccine Tweets with Sentiment Annotation (CVSA), the highest accuracy obtained is 93.0%, for the AstraZeneca vaccine dataset 90.94%, for the Pfizer vaccine dataset 91.07%, 88.01% accuracy for the Moderna vaccine dataset, for the Sinovac vaccine dataset 92.8% accuracy, and 93.87% accuracy for the Sinopharm vaccine dataset, respectively. The quantitative comparisons demonstrate that the proposed research achieves better accuracy as compared to state-of-the-art research.<\/jats:p>","DOI":"10.3390\/computation10080141","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T21:39:21Z","timestamp":1660858761000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["COVID-19 Vaccines Related User\u2019s Response Categorization Using Machine Learning Techniques"],"prefix":"10.3390","volume":"10","author":[{"given":"Ahmed","family":"Shahzad","sequence":"first","affiliation":[{"name":"Department of Computer Science, Government College University, Faisalabad 38000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8869-3037","authenticated-orcid":false,"given":"Bushra","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Government College University, Faisalabad 38000, Pakistan"}]},{"given":"Nouman","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Mirpur University of Science & Technology (MUST), Mirpur 10250, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4555-2389","authenticated-orcid":false,"given":"Uzma","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Government College University, Faisalabad 38000, Pakistan"}]},{"given":"Abdulaziz Jarallah","family":"Alghadhban","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer Science and Information, King Suad University, Riyadh 11451, Saudi Arabia"}]},{"given":"Muhammad","family":"Assam","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Science and Technology, Bannu 28100, Pakistan"}]},{"given":"Nivin A.","family":"Ghamry","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Giza 3750010, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3151-9967","authenticated-orcid":false,"given":"Elsayed Tag","family":"Eldin","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Future University in Egypt New Cairo, New Cairo 11835, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1002\/ima.22541","article-title":"A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage","volume":"31","author":"Khalil","year":"2021","journal-title":"Int. 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