Published on in Vol 7, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24585, first published .
Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

Authors of this article:

Tiago de Melo1 Author Orcid Image ;   Carlos M S Figueiredo1 Author Orcid Image

Journals

  1. Lee K, Kim B, Nan D, Kim J. Structural Topic Model Analysis of Mask-Wearing Issue Using International News Big Data. International Journal of Environmental Research and Public Health 2021;18(12):6432 View
  2. Biancovilli P, Makszin L, Jurberg C. Misinformation on social networks during the novel coronavirus pandemic: a quali-quantitative case study of Brazil. BMC Public Health 2021;21(1) View
  3. Quach H, Pham T, Hoang N, Phung D, Nguyen V, Le S, Le T, Bui T, Le D, Dang A, Tran D, Ngu N, Vogt F, Nguyen C, Sung W. Using ‘infodemics’ to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July—September 2020. PLOS ONE 2022;17(4):e0266299 View
  4. Beliga S, Martinčić-Ipšić S, Matešić M, Petrijevčanin Vuksanović I, Meštrović A. Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing. JMIR Public Health and Surveillance 2021;7(12):e31540 View
  5. Jafarzadeh H, Pauleen D, Abedin E, Weerasinghe K, Taskin N, Coskun M, Mehmood R. Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter. PLOS ONE 2021;16(12):e0259882 View
  6. Bendezu-Quispe G, Benites-Meza J, Urrunaga-Pastor D, Herrera-Añazco P, Uyen-Cateriano A, Rodriguez-Morales A, Toro-Huamanchumo C, Hernandez A, Benites-Zapata V. Mass Media Use to Learn About COVID-19 and the Non-intention to Be Vaccinated Against COVID-19 in Latin America and Caribbean Countries. Frontiers in Medicine 2022;9 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. Gori Maia A, Martinez J, Marteleto L, Rodrigues C, Sereno L. Can the Content of Social Networks Explain Epidemic Outbreaks?. Population Research and Policy Review 2023;42(1) View
  9. Kuo H, Chen S, Lai Y. Investigating COVID-19 News before and after the Soft Lockdown: An Example from Taiwan. Sustainability 2021;13(20):11474 View
  10. Shahi T, Sitaula C, Paudel N, G T. A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification. Computational Intelligence and Neuroscience 2022;2022:1 View
  11. Christensen B, Laydon D, Chelkowski T, Jemielniak D, Vollmer M, Bhatt S, Krawczyk K. Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study. JMIR Infodemiology 2022;2(2):e35121 View
  12. Seródio Figueiredo C, de Melo T, Goes R. Evaluating Voice Assistants' Responses to COVID-19 Vaccination in Portuguese: Quality Assessment. JMIR Human Factors 2022;9(1):e34674 View
  13. Chang V, Ng C, Xu Q, Guizani M, Hossain M. How Do People View COVID-19 Vaccines. Journal of Global Information Management 2022;30(10):1 View
  14. Mandl T, Jaki S, Mitera H, Schmidt F. Interdisciplinary Analysis of Science Communication on Social Media during the COVID-19 Crisis. Knowledge 2023;3(1):97 View
  15. Hong Y, Xie F, An X, Lan X, Liu C, Yan L, Zhang H. Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts. Journal of Medical Internet Research 2023;25:e42671 View
  16. Babić K, Petrović M, Beliga S, Martinčić-Ipšić S, Matešić M, Meštrović A. Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model. Applied Sciences 2021;11(21):10442 View
  17. Oliveira F, Haque A, Mougouei D, Evans S, Sichman J, Singh M. Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modeling and Emotion Classification Approach. IEEE Access 2022;10:16883 View
  18. Li W, Deng X, Shao H, Wang X. Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey. Computer Modeling in Engineering & Sciences 2021;129(1):65 View
  19. Sitaula C, Basnet A, Mainali A, Shahi T, G T. Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets. Computational Intelligence and Neuroscience 2021;2021:1 View
  20. Almomani H, Patel N, Donyai P. News Media Coverage of the Problem of Purchasing Fake Prescription Medicines on the Internet: Thematic Analysis. JMIR Formative Research 2023;7:e45147 View
  21. Chin H, Lima G, Shin M, Zhunis A, Cha C, Choi J, Cha M. User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e40922 View
  22. BAYRAM U. Revealing the Reflections of the Pandemic by Investigating COVID-19 Related News Articles Using Machine Learning and Network Analysis. Bilişim Teknolojileri Dergisi 2022;15(2):209 View
  23. Lu G, Businger M, Dollfus C, Wozniak T, Fleck M, Heroth T, Lock I, Lipenkova J. Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing. International Journal of Data Science and Analytics 2023;15(3):291 View
  24. da Silva Lima K, Do Bú E, Silva W, Miranda M, Pereira C. COVID‐19 Vaccination Acceptance: A Case of Interplay Between Political and Health Dimensions. Political Psychology 2023;44(4):917 View
  25. Evans S, Jones R, Alkan E, Sichman J, Haque A, de Oliveira F, Mougouei D, Yan Z. The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing. Human Behavior and Emerging Technologies 2023;2023:1 View
  26. Khalid E, Salah Khalefa M, Yassen W, Adil Yassin A. Omicron virus emotions understanding system based on deep learning architecture. Journal of Ambient Intelligence and Humanized Computing 2023;14(7):9497 View
  27. Verjovsky M, Barreto M, Carmo I, Coutinho B, Thomer L, Lifschitz S, Jurberg C. Political quarrel overshadows vaccination advocacy: How the vaccine debate on Brazilian Twitter was framed by anti-vaxxers during Bolsonaro administration. Vaccine 2023;41(39):5715 View
  28. Vianna D, Carneiro F, Carvalho J, Plastino A, Paes A. Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models. Language Resources and Evaluation 2024;58(1):223 View
  29. Zhang Q, Yang J, Niu T, Wen K, Hong X, Wu Y, Wang M. Analysis of the evolving factors of social media users’ emotions and behaviors: a longitudinal study from China’s COVID-19 opening policy period. BMC Public Health 2023;23(1) View
  30. Dasgupta P, Amin J, Paris C, MacIntyre C. News Coverage of Face Masks in Australia During the Early COVID-19 Pandemic: Topic Modeling Study. JMIR Infodemiology 2023;3:e43011 View
  31. 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
  32. de Lima B, Baracho R, Mandl T, Porto P. Reactions to science communication: discovering social network topics using word embeddings and semantic knowledge. Social Network Analysis and Mining 2023;13(1) View
  33. Sitaula C, Shahi T. Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features. Journal of Ambient Intelligence and Humanized Computing 2024;15(3):2047 View
  34. Zhou Q, Xu Y, Yang L, Menhas R. Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias. International Journal of Nursing Sciences 2024;11(1):99 View
  35. C. P, P. M. D. An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling. Journal of Advances in Information Technology 2023;14(6):1390 View
  36. Wang Y, Wang X, Zhang J, Shi M, Wanta W. Tracking attention about COVID-19 vaccines on twitter and newspapers: A dynamic agenda-setting approach. Telematics and Informatics Reports 2024;13:100122 View
  37. Claes M, Farooq U, Salman I, Teern A, Isomursu M, Halonen R. Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic. SN Computer Science 2024;5(2) View
  38. Park D, Kim D, Park A. Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022. Journal of Medical Internet Research 2024;26:e50518 View
  39. Sousa‐Pinto B, Jankin S, Vieira R, Marques‐Cruz M, Fonseca J, Bousquet J. English tweets on allergy: Content analysis and association with surveillance data. Clinical & Experimental Allergy 2024 View

Books/Policy Documents

  1. Rahman M, Islam M. Sentimental Analysis and Deep Learning. View
  2. Küçük D, Arıcı N. Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media. View
  3. Juanals B, Minel J. Advances in Computational Collective Intelligence. View
  4. de Lima B, Baracho R, Mandl T. Information Systems and Technologies. View
  5. Juanals B, Minel J. Intelligent Sustainable Systems. View