Temporal Evolution of Public Health Sentiment: A Longitudinal Analysis
Date
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Press
Abstract
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.Description
Keywords
Digital Health, Disaster Response, NLP, Public Health, Text Mining, 46 Information and Computing Sciences, 4608 Human-Centred Computing, Infectious Diseases, Emerging Infectious Diseases, 0807 Library and Information Studies, 1117 Public Health and Health Services, Medical Informatics, 4203 Health services and systems, 4601 Applied computing
Source
Studies in Health Technology and Informatics, ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, Volume 329: MEDINFO 2025 — Healthcare Smart × Medicine Deep, 1505-1509. doi: 10.3233/SHTI251090
Publisher's version
Rights statement
© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).