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

Early Results in Infodemiology and Infoveillance (77) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) JMIR Theme Issue: COVID-19 Special Issue (2461) Infodemiology and Infoveillance (1369) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050)

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 (14) Tweetations (6) Metrics

Surani Matharaarachchi   1 , BSc, MSc ;   Mike Domaratzki   2 , BMATH, MMATH, PhD ;   Alan Katz   3 , MBChB, MSc, CCFP ;   Saman Muthukumarana   1 , BSc, MSc, PhD

1 Department of Statistics, University of Manitoba, Winnipeg, MB, Canada

2 Department of Computer Science, Western University, London, ON, Canada

3 Departments of Community Health Sciences and Family Medicine, University of Manitoba, Winnipeg, MB, Canada

Corresponding Author:

  • Surani Matharaarachchi, BSc, MSc
  • Department of Statistics
  • University of Manitoba
  • 66 Chancellors Cir
  • Winnipeg, MB, R3T 2N2
  • Canada
  • Phone: 1 4313385077
  • Email: matharas@myumanitoba.ca