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

Formative Evaluation of Digital Health Interventions (2410) Depression and Mood Disorders; Suicide Prevention (1363) Machine Learning (1465) Artificial Intelligence (1371) Natural Language Processing (692) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Methods and New Tools in Mental Health Research (454)

Published on in Vol 6, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36118, first published .
Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques

Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques

Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques

Authors of this article:

Muhammad Khubayeeb Kabir1 Author Orcid Image ;   Maisha Islam1 Author Orcid Image ;   Anika Nahian Binte Kabir1 Author Orcid Image ;   Adiba Haque1 Author Orcid Image ;   Md Khalilur Rhaman2 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Garg M. Towards Mental Health Analysis in Social Media for Low-resourced Languages. ACM Transactions on Asian and Low-Resource Language Information Processing 2024;23(3):1 View
  2. Zhang W, Kong L, Lee S, Chen Y, Zhang G, Wang H, Song M. Detecting mental and physical disorders using multi-task learning equipped with knowledge graph attention network. Artificial Intelligence in Medicine 2024;149:102812 View
  3. Turjo M, Mundada K, Haque N, Ahmed N. Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study. JMIR Research Protocols 2024;13:e55511 View
  4. Khan A, Ali R. Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media. Social Network Analysis and Mining 2024;14(1) View
  5. Hoque M, Salma U, Uddin M, Shampa S. Depression Intensity Identification using Transformer Ensemble Technique for the Resource-constrained Bengali Language. Journal of Engineering Advancements 2024:27 View
  6. van Buchem M, de Hond A, Fanconi C, Shah V, Schuessler M, Kant I, Steyerberg E, Hernandez-Boussard T. Applying natural language processing to patient messages to identify depression concerns in cancer patients. Journal of the American Medical Informatics Association 2024;31(10):2255 View
  7. Alhuzali H, Alasmari A, Alsaleh H. MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare. IEEE Access 2024;12:101155 View
  8. Akter Asma S, Akhter N, Sharmin S, Rahman M, Sanwar Hosen A, Lee O, Ra I. Hierarchical Explainable Network for Investigating Depression From Multilingual Textual Data. IEEE Access 2024;12:131915 View
  9. Myee M, Rebekah R, Deepa T, Zion G, Lokesh K. Detection of Depression in Social Media Posts using Emotional Intensity Analysis. Engineering, Technology & Applied Science Research 2024;14(5):16207 View

Books/Policy Documents

  1. Gorrab A, Ben Rabah N, Le Grand B, Deneckère R, Bonnerot T. Advanced Information Networking and Applications. View
  2. Gorrab A, Bonnerot T. Intelligent Systems and Applications. View