Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26953, first published .
Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Journals

  1. Yousefinaghani S, Dara R, Mubareka S, Papadopoulos A, Sharif S. An analysis of COVID-19 vaccine sentiments and opinions on Twitter. International Journal of Infectious Diseases 2021;108:256 View
  2. Hu T, Wang S, Luo W, Zhang M, Huang X, Yan Y, Liu R, Ly K, Kacker V, She B, Li Z. Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective. Journal of Medical Internet Research 2021;23(9):e30854 View
  3. Wang Y, Shi M, Zhang J, Feng G. What public health campaigns can learn from people’s Twitter reactions on mask-wearing and COVID-19 Vaccines: a topic modeling approach. Cogent Social Sciences 2021;7(1) View
  4. Marcec R, Likic R. Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgraduate Medical Journal 2022;98(1161):544 View
  5. Cevik E, Kirci Altinkeski B, Cevik E, Dibooglu S. Investor sentiments and stock markets during the COVID-19 pandemic. Financial Innovation 2022;8(1) View
  6. Xu W, Tshimula J, Dubé È, Graham J, Greyson D, MacDonald N, Meyer S. Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach. JMIR Infodemiology 2022;2(2):e41198 View
  7. Hu M, Conway M. Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia. JMIR Infodemiology 2022;2(2):e36941 View
  8. Hagen L, Fox A, O'Leary H, Dyson D, Walker K, Lengacher C, Hernandez R. The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding. JMIR Infodemiology 2022;2(1):e34231 View
  9. Zang S, Zhang X, Xing Y, Chen J, Lin L, Hou Z. Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review. Journal of Medical Internet Research 2023;25:e40057 View
  10. Kobayashi R, Takedomi Y, Nakayama Y, Suda T, Uno T, Hashimoto T, Toyoda M, Yoshinaga N, Kitsuregawa M, Rocha L. Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis. Journal of Medical Internet Research 2022;24(12):e41928 View
  11. Karami A, Zhu M, Goldschmidt B, Boyajieff H, Najafabadi M. COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter. Vaccines 2021;9(10):1059 View
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  14. Zou H, Xiang K. Sentiment Classification Method Based on Blending of Emoticons and Short Texts. Entropy 2022;24(3):398 View
  15. Park S, Suh Y. A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e42623 View
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  17. Yousef M, Dietrich T, Rundle-Thiele S. Actions Speak Louder Than Words: Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake. JMIR Formative Research 2022;6(9):e37775 View
  18. Wang A, Lan J, Wang M, Yu C. The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study. JMIR Medical Informatics 2021;9(11):e30467 View
  19. Zhang J, Wang Y, Shi M, Wang X. Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study. JMIR Public Health and Surveillance 2021;7(12):e32814 View
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  33. Yin H, Song X, Yang S, Li J. Sentiment analysis and topic modeling for COVID-19 vaccine discussions. World Wide Web 2022;25(3):1067 View
  34. 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
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  38. Aljedaani W, Saad E, Rustam F, de la Torre Díez I, Ashraf I. Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends. Mathematics 2022;10(17):3199 View
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