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

Applications of AI (230)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49531, first published .
Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Authors of this article:

Ali Akbar Jamali1 Author Orcid Image ;   Corinne Berger1 Author Orcid Image ;   Raymond J Spiteri1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Azizi M, Jamali A, Spiteri R. Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach. JMIR Formative Research 2024;8:e49562 View
  2. Jamali A, Berger C, Spiteri R. Identification of depression predictors from standard health surveys using machine learning. Current Research in Behavioral Sciences 2024;7:100157 View
  3. Shah S, Gillani S, Baig M, Saleem M, Siddiqui M. Advancing depression detection on social media platforms through fine-tuned large language models. Online Social Networks and Media 2025;46:100311 View
  4. Chen H, Alfred M, Cohen E. Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study. JMIR Medical Informatics 2025;13:e68955 View

Conference Proceedings

  1. Chen Z, Lin J, Jiang J, Jing D. 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). A Method to Classify Emotions Based on BERT View