Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49300, first published .
Temporal and Emotional Variations in People’s Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data

Temporal and Emotional Variations in People’s Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data

Temporal and Emotional Variations in People’s Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data

Authors of this article:

Jing Dai1 Author Orcid Image ;   Fang Lyu1 Author Orcid Image ;   Lin Yu1 Author Orcid Image ;   Yunyu He2 Author Orcid Image

Journals

  1. Fallatah D, Adekola H. Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks. Infection Prevention in Practice 2024;6(3):100382 View
  2. Khorram-Manesh A, Burkle Jr F, Goniewicz K. Pandemics: past, present, and future: multitasking challenges in need of cross-disciplinary, transdisciplinary, and multidisciplinary collaborative solutions. Osong Public Health and Research Perspectives 2024;15(4):267 View
  3. Omojowo F, Osaghae E, Kolajo T. Evaluating COVID-19 public discourse for sentiment, topic, and geolocation analysis. Journal of Electrical Systems and Information Technology 2025;12(1) View
  4. Soltani M, Sharif S, Dara R. Analyzing social media discourse of avian influenza outbreaks. Natural Language Processing Journal 2025;12:100176 View
  5. Lai S, Dong J, Lin Z, Peng Y, Long X, Hao L, Li J, Huang J, He W, Long X, Luo M, Huang K, Ren A. Sentiment analysis based on machine learning for infodemic during the mpox epidemic. Humanities and Social Sciences Communications 2025 View