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

JMIR Theme Issue: COVID-19 Special Issue (2452) Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Natural Language Processing (692) Information Quality in Digital Media (340) Vaccination and Immunization in the Digital Age (374) Quality/Credibility of eHealth Information and Trust Issues (531)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40408, first published .
Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study

Authors of this article:

Chad A Melton1, 2 Author Orcid Image ;   Brianna M White2 Author Orcid Image ;   Robert L Davis2 Author Orcid Image ;   Robert A Bednarczyk3 Author Orcid Image ;   Arash Shaban-Nejad2 Author Orcid Image
Article Authors Cited by (14) Tweetations (7) Metrics

Journals

  1. Olusanya O, White B, Malik F, Hester K, Davis R, Bednarczyk R, Shaban-Nejad A, Fischer F. Healthcare professionals’ perceptions and recommendations regarding adolescent vaccinations in Georgia and Tennessee during the COVID-19 pandemic: A qualitative research. PLOS ONE 2022;17(11):e0277748 View
  2. Catelli R, Pelosi S, Comito C, Pizzuti C, Esposito M. Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy. Computers in Biology and Medicine 2023;158:106876 View
  3. Wang A, Dara R, Yousefinaghani S, Maier E, Sharif S. A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception. Big Data and Cognitive Computing 2023;7(2):72 View
  4. White B, Melton C, Zareie P, Davis R, Bednarczyk R, Shaban-Nejad A. Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis. BMJ Health & Care Informatics 2023;30(1):e100665 View
  5. García-Rudolph A, Sanchez-Pinsach D, Frey D, Opisso E, Cisek K, Kelleher J. Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus. Applied Sciences 2023;13(11):6713 View
  6. Franceschi J, Pareschi L, Bellodi E, Gavanelli M, Bresadola M, Vellucci P. Modeling opinion polarization on social media: Application to Covid-19 vaccination hesitancy in Italy. PLOS ONE 2023;18(10):e0291993 View
  7. Wermuth M, Sambre P. The terminological impact of pandemics. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication 2023;29(2):169 View
  8. 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
  9. Aldosery A, Carruthers R, Kay K, Cave C, Reynolds P, Kostkova P. Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model. Frontiers in Public Health 2024;12 View
  10. Li Z, Zou Z. Punctuation and lexicon aid representation: A hybrid model for short text sentiment analysis on social media platform. Journal of King Saud University - Computer and Information Sciences 2024;36(3):102010 View
  11. Riva M, Parigi T, Ungaro F, Massimino L. Hugging Face's impact on medical applications of artificial intelligence. Computational and Structural Biotechnology Reports 2024;1:100003 View
  12. Bottacin W, Luquetta A, Gomes-Jr L, de Souza T, Reis W, Melchiors A. Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences. International Journal of Clinical Pharmacy 2024 View

Books/Policy Documents

  1. Shaban-Nejad A, Michalowski M, Bianco S. Artificial Intelligence for Personalized Medicine. View
  2. Anoop V, Thekkiniath J, Govindarajan U. Multi-disciplinary Trends in Artificial Intelligence. View