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

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
  3. Xue H, Zhang L, Liang H, Kuang L, Han H, Yang X, Guo L. Influenza trend prediction method combining Baidu index and support vector regression based on an improved particle swarm optimization algorithm. AIMS Mathematics 2023;8(11):25528 View
  4. Lossio-Ventura J, Weger R, Lee A, Guinee E, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150 View
  5. Huang G, Li Y, Jameel S, Long Y, Papanastasiou G. From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?. Computational and Structural Biotechnology Journal 2024;24:362 View