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

Early Results in Infodemiology and Infoveillance (39) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1404) JMIR Theme Issue: COVID-19 Special Issue (2243) Infodemiology and Infoveillance (1128) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (924)

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 Matharaarachchi 1 Author Orcid Image ;   Mike Domaratzki 2 Author Orcid Image ;   Alan Katz 3 Author Orcid Image ;   Saman Muthukumarana 1 Author Orcid Image
Article Authors Cited by (3) 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