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

Early Results in Infodemiology and Infoveillance (72) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) JMIR Theme Issue: COVID-19 Special Issue (2452) Infodemiology and Infoveillance (1349) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 6, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37984, first published .
Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets

Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets

Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets

Authors of this article:

Surani Matharaarachchi1 Author Orcid Image ;   Mike Domaratzki2 Author Orcid Image ;   Alan Katz3 Author Orcid Image ;   Saman Muthukumarana1 Author Orcid Image
Article Authors Cited by (13) Tweetations (6) Metrics

Journals

  1. Rizwan Rashid Rana M, Ur Rehman S, Nawaz A, Ali T, Imran A, Alzahrani A, Almuhaimeed A. Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework. Computer Systems Science and Engineering 2023;46(2):2415 View
  2. Malden D, Tartof S, Ackerson B, Hong V, Skarbinski J, Yau V, Qian L, Fischer H, Shaw S, Caparosa S, Xie F. Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System. JMIR Public Health and Surveillance 2022;8(12):e41529 View
  3. Zhang Y, Hong J, Chen S. Medical Big Data and Artificial Intelligence for Healthcare. Applied Sciences 2023;13(6):3745 View
  4. Dehghani M, Yazdanparast Z. Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining. Informatics in Medicine Unlocked 2023;42:101351 View
  5. Klein A, Kunatharaju S, O'Connor K, Gonzalez-Hernandez G. Automatically Identifying Self-Reports of COVID-19 Diagnosis on Twitter: An Annotated Data Set, Deep Neural Network Classifiers, and a Large-Scale Cohort. Journal of Medical Internet Research 2023;25:e46484 View
  6. Tian Y, Zhang W, Duan L, McDonald W, Osgood N. Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Frontiers in Digital Health 2023;5 View
  7. Somayajula S, Litake O, Liang Y, Hosseini R, Nemati S, Wilson D, Weinreb R, Malhotra A, Xie P. Improving long COVID-related text classification: a novel end-to-end domain-adaptive paraphrasing framework. Scientific Reports 2024;14(1) View
  8. Ahmad I, Amelio A, Merla A, Scozzari F. A survey on the role of artificial intelligence in managing Long COVID. Frontiers in Artificial Intelligence 2024;6 View
  9. Kusuma I, Suherman S. The Pulse of Long COVID on Twitter: A Social Network Analysis. Archives of Iranian Medicine 2024;27(1):36 View
  10. Chepo M, Martin S, Déom N, Khalid A, Vindrola-Padros C. Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e50139 View
  11. Chaowchuen S, Warin K, Somyanonthanakul R, Panichkitkosolkul W, Suebnukarn S. The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining. European Journal of Dentistry 2024;18(03):907 View
  12. Mayo N, Hum S, Matout M, Fellows L, Brouillette M. Portrait of mental health identified by people with the post-covid syndrome. Quality of Life Research 2024;33(9):2509 View
  13. El-Toukhy S, Hegeman P, Zuckerman G, Das A, Moses N, Troendle J, Powell-Wiley T. Study of Postacute Sequelae of COVID-19 Using Digital Wearables: Protocol for a Prospective Longitudinal Observational Study. JMIR Research Protocols 2024;13:e57382 View