{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:19Z","timestamp":1755219859534,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>This study advances our understanding of public health crisis communication by conducting a longitudinal analysis. As COVID-19 has been the largest public health crisis to date, we performed sentiment analysis on it. While previous research focused on discrete time periods, our study examines the arc of pandemic-related discourse from 2020 to 2022, revealing long-term patterns in public sentiment evolution. Using advanced natural language processing techniques and temporal pattern analysis, we identify key transition points in public health discourse and sentiment, offering insights for future crisis communication strategies.<\/jats:p>","DOI":"10.3233\/shti251090","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:40:42Z","timestamp":1754566842000},"source":"Crossref","is-referenced-by-count":0,"title":["Temporal Evolution of Public Health Sentiment: A Longitudinal Analysis"],"prefix":"10.3233","author":[{"given":"Samaneh","family":"Madanian","sequence":"first","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, AUT, Auckland, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vahid","family":"Bakhtiari","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of West London, London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, AUT, Auckland, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251090","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:40:43Z","timestamp":1754566843000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251090","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}