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

Theme Issue: Medical Informatics and COVID-19 (99) JMIR Theme Issue: COVID-19 Special Issue (2241) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (922)

Published on in Vol 10 , No 5 (2022) :May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34306, first published .
Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set

Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set

Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set

Authors of this article:

Adrien Boukobza 1, 2, 3 Author Orcid Image ;   Anita Burgun 1, 2, 3 Author Orcid Image ;   Bertrand Roudier 4 Author Orcid Image ;   Rosy Tsopra 1, 2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Ehiabhi J, Wang H. A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals. BioMedInformatics 2023;3(1):193 View
  2. Ueda M, Watanabe K, Sueki H. Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm. Journal of Medical Internet Research 2023;25:e44965 View