Published on in Vol 21, No 8 (2019): August

Preprints (earlier versions) of this paper are available at, first published .
Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective

Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective

Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective


  1. Chen X, Xie H. A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature. Cognitive Computation 2020;12(6):1097 View
  2. Dadkhah M, Lagzian M, Rahimnia F, Kimiafar K. What Do Websites Say about Internet of Things Challenges? A Text Mining Approach. Science & Technology Libraries 2020;39(2):125 View
  3. Petersen C, Halter R, Kotz D, Loeb L, Cook S, Pidgeon D, Christensen B, Batsis J. Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study. JMIR mHealth and uHealth 2020;8(8):e16862 View
  4. Dalanon J, Matsuka Y. Forecasting Interest in Health Professions Education Based on Relative Search Volume Trends From the Philippines. Health Professions Education 2020;6(3):368 View
  5. Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, Zheng X, Wang T, Lei J. Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed. Journal of Medical Internet Research 2020;22(1):e16816 View
  6. Nguyen A, Trinh X, Wang S, Wu A. Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions. Journal of Medical Internet Research 2021;23(5):e20803 View
  7. Pérez-Pérez M, Igrejas G, Fdez-Riverola F, Lourenço A. A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era. Artificial Intelligence in Medicine 2021:102131 View
  8. Tirdad K, Dela Cruz A, Sadeghian A, Cusimano M. A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports. Brain Informatics 2021;8(1) View
  9. Al-Rawi A, Grepin K, Li X, Morgan R, Wenham C, Smith J. Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data. Journal of Healthcare Informatics Research 2021;5(3):249 View
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  11. Kumar J, Trueman T, Cambria E. Gender-based multi-aspect sentiment detection using multilabel learning. Information Sciences 2022;606:453 View
  12. Choi-Kwon S, Kim J. Anger, a Result and Cause of Stroke: A Narrative Review. Journal of Stroke 2022;24(3):311 View
  13. Zhao K, Zhou L, Zhao X. Multi-modal emotion expression and online charity crowdfunding success. Decision Support Systems 2022;163:113842 View
  14. Klein L, Kumar A, Wolff A, Naqvi B. Understanding the role of digitalization and social media on energy citizenship. Open Research Europe 2023;3:6 View
  15. Brunskill A, Gilbert E. Academic libraries' social media posts related to disabilities: A review of libraries' tweets in terms of their content and accessibility. The Journal of Academic Librarianship 2023;49(3):102684 View
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Books/Policy Documents

  1. Rana D, Saini N. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance. View
  2. Subirats L, Piella G. Sex and Gender Bias in Technology and Artificial Intelligence. View
  3. Sridevi U, Sophia S. Big Data Analytics in Cognitive Social Media and Literary Texts. View
  4. Saini N, Rana D. Futuristic Trends in Networks and Computing Technologies. View
  5. Agbozo E, Watat J, Olaleye S. Data Science for COVID-19. View
  6. Kim J. Neuropsychiatric Manifestations in Neurological Diseases. View