{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:58:52Z","timestamp":1772301532947,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO\u2019s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government\u2019s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Na\u00efve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.<\/jats:p>","DOI":"10.3390\/info12050204","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T04:05:32Z","timestamp":1620705932000},"page":"204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Na\u00efve Bayes"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-8727","authenticated-orcid":false,"given":"Charlyn","family":"Villavicencio","sequence":"first","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"},{"name":"College of Information and Communications Technology, Bulacan State University, Bulacan 3000, Philippines"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2738-4932","authenticated-orcid":false,"given":"Julio Jerison","family":"Macrohon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}]},{"given":"X. Alphonse","family":"Inbaraj","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0026-8333","authenticated-orcid":false,"given":"Jyh-Horng","family":"Jeng","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}]},{"given":"Jer-Guang","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jan, A., Mata, M.N., Albinsson, P.A., Martins, J.M., Hassan, R.B., and Mata, P.N. (2021). Alignment of Islamic Banking Sustainability Indicators with Sustainable Development Goals: Policy Recommendations for Addresing the COVID-19 Pandemic. 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