This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2502) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1532) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1806) Infodemiology and Infoveillance (1469) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1089)

Published on in Vol 24, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40323, first published .
Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Authors of this article:

Hassan Alhuzali1 Author Orcid Image ;   Tianlin Zhang2 Author Orcid Image ;   Sophia Ananiadou2, 3 Author Orcid Image
Article Authors Cited by (20) Tweetations (14) Metrics

Journals

  1. Amujo O, Ibeke E, Fuzi R, Ogara U, Iwendi C. Sentiment Computation of UK-Originated COVID-19 Vaccine Tweets: A Chronological Analysis and News Effect. Sustainability 2023;15(4):3212 View
  2. Pucci F, Fedele P, Dimitri G. Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic. Cognitive Computation and Systems 2023;5(1):71 View
  3. Jiménez-Cabas J, Torres L, Lozoya-Santos J. Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks. Sustainability 2023;15(6):5113 View
  4. Thakur N. Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox. Big Data and Cognitive Computing 2023;7(2):116 View
  5. Isip Tan I, Cleofas J, Solano G, Pillejera J, Catapang J. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study. JMIR Formative Research 2023;7:e41134 View
  6. Kodati D, Dasari C. Negative emotion detection on social media during the peak time of COVID-19 through deep learning with an auto-regressive transformer. Engineering Applications of Artificial Intelligence 2024;127:107361 View
  7. Mori Y, Miyatake N, Suzuki H, Mori Y, Okada S, Tanimoto K. Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining. Vaccines 2023;11(8):1327 View
  8. Fernández-Pichel M, Aragón M, Saborido-Patiño J, Losada D. Personality trait analysis during the COVID-19 pandemic: a comparative study on social media. Journal of Intelligent Information Systems 2024;62(1):117 View
  9. Shah H, Househ M. Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map. BMJ Health & Care Informatics 2023;30(1):e100728 View
  10. Skarpelos Y, Messini S, Roinioti E, Karpouzis K, Kaperonis S, Marazoti M. Emotions during the Pandemic’s First Wave: The Case of Greek Tweets. Digital 2024;4(1):126 View
  11. Lam B, Chu A, Chan J, So M. Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data. Health Data Science 2024;4 View
  12. Shah H, Agus M, Househ M. Sentiment visualization of correlation of loneliness mapped through social intelligence analysis. Computer Methods and Programs in Biomedicine Update 2024;5:100144 View
  13. Hummell E, Borg S, Foster M, Burns K, Harris Rimmer S. Agendas of Reform: Continuity and Change in Australia’s National Disability Insurance Scheme (NDIS). Social Policy and Society 2024:1 View
  14. Li Y, Zeng Z, Yu L. Withdrawn: The crisis communication of the COVID-19 pandemic in media discourse: Text mining for infectious disease frames and environmental pollution. AQUA — Water Infrastructure, Ecosystems and Society 2024 View
  15. Zamsuri A, Defit S, Nurcahyo G. Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning. Journal of Advances in Information Technology 2024;15(4):519 View
  16. Alhuzali H, Alasmari A, Alsaleh H. MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare. IEEE Access 2024;12:101155 View
  17. Cho S, Hisamitsu S, Jin H, Toyoda M, Yoshinaga N, Gupta A. Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users. PLOS ONE 2024;19(12):e0299935 View
  18. Dilanka R, Rupasingha R. Sentiment Analysis on Suicidal Tendency Affected by the COVID -19 Pandemic: A Comparison of Different Algorithms using Twitter Data. Coronaviruses 2025;6(2) View
  19. Carpenter K, Nguyen A, Smith D, Samori I, Humphreys K, Lembke A, Kiang M, Eichstaedt J, Altman R, Villanueva C. Which social media platforms facilitate monitoring the opioid crisis?. PLOS Digital Health 2025;4(4):e0000842 View

Conference Proceedings

  1. Mirshafee M, Barcomb A, Tan B. 2023 IEEE Symposium Series on Computational Intelligence (SSCI). Advancing Smart Cities Through Novel Social Media Text Analysis: A Case Study of Calgary View