Published on in Vol 21, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13456, first published .
Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology

Authors of this article:

Jeongah On1 Author Orcid Image ;   Hyeoun-Ae Park1, 2 Author Orcid Image ;   Tae-Min Song3 Author Orcid Image

Journals

  1. Matza L, Paulus T, Garris C, Van de Velde N, Chounta V, Deger K. Qualitative Thematic Analysis of Social Media Data to Assess Perceptions of Route of Administration for Antiretroviral Treatment among People Living with HIV. The Patient - Patient-Centered Outcomes Research 2020;13(4):409 View
  2. Lee J, Park H, Park S, Song T. Using Social Media Data to Understand Consumers' Information Needs and Emotions Regarding Cancer: Ontology-Based Data Analysis Study. Journal of Medical Internet Research 2020;22(12):e18767 View
  3. Singh L, Singh S. Empirical study of sentiment analysis tools and techniques on societal topics. Journal of Intelligent Information Systems 2021;56(2):379 View
  4. Della Marca R, d’Onofrio A. Volatile opinions and optimal control of vaccine awareness campaigns: chaotic behaviour of the forward-backward sweep algorithm vs. heuristic direct optimization. Communications in Nonlinear Science and Numerical Simulation 2021;98:105768 View
  5. Lee J, Park H, Song T. A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology. Journal of Medical Internet Research 2021;23(6):e25028 View
  6. Liu R, Huang Y, Sun J, Lau J, Cai Q. A Shot in the Arm for Vaccination Intention: The Media and the Health Belief Model in Three Chinese Societies. International Journal of Environmental Research and Public Health 2022;19(6):3705 View
  7. Alamoodi A, Zaidan B, Al-Masawa M, Taresh S, Noman S, Ahmaro I, Garfan S, Chen J, Ahmed M, Zaidan A, Albahri O, Aickelin U, Thamir N, Fadhil J, Salahaldin A. Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Computers in Biology and Medicine 2021;139:104957 View
  8. Aygun I, Kaya B, Kaya M. Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning. IEEE Journal of Biomedical and Health Informatics 2022;26(5):2360 View
  9. Teng S, Jiang N, Khong K. Using big data to understand the online ecology of COVID-19 vaccination hesitancy. Humanities and Social Sciences Communications 2022;9(1) View
  10. Daradkeh M. Organizational Adoption of Sentiment Analytics in Social Media Networks. International Journal of Information Technologies and Systems Approach 2022;15(2):1 View
  11. Alotaibi W, Alomary F, Mokni R. COVID-19 vaccine rejection causes based on Twitter people’s opinions analysis using deep learning. Social Network Analysis and Mining 2023;13(1) View
  12. Della Marca R, d’Onofrio A, Sensi M, Sottile S. A geometric analysis of the impact of large but finite switching rates on vaccination evolutionary games. Nonlinear Analysis: Real World Applications 2024;75:103986 View
  13. Bennett V, Spasić I, Filimonov M, Muralidaran V, Kemp A, Allen S, Watkins W. The feasibility of using parent’s social media conversations to inform burn first aid interventions: Mixed methods study (Preprint). JMIR Formative Research 2023 View

Books/Policy Documents

  1. Goyal N, El-Taliawi O, Howlett M. Emerging Pedagogies for Policy Education. View
  2. Kumar K, Pande B. Disease Control Through Social Network Surveillance. View
  3. Jain S, Dalal S, Dave M. Semantic Intelligence. View