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

Depression and Mood Disorders; Suicide Prevention (1363) Data Science (270) Methods and New Tools in Mental Health Research (454) Diagnostic Tools in Mental Health (242) Machine Learning (1465) New Methods (945) Artificial Intelligence (1371)

Published on in Vol 8, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19824, first published .
Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study

Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study

Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study

Authors of this article:

Akkapon Wongkoblap1, 2, 3 Author Orcid Image ;   Miguel A Vadillo4, 5 Author Orcid Image ;   Vasa Curcin1, 4 Author Orcid Image
Article Authors Cited by (22) Tweetations (7) Metrics

Journals

  1. Singh A, Singh J. Synthesis of Affective Expressions and Artificial Intelligence to Discover Mental Distress in Online Community. International Journal of Mental Health and Addiction 2024;22(4):1921 View
  2. Yadav U, Sharma A. A novel automated depression detection technique using text transcript. International Journal of Imaging Systems and Technology 2023;33(1):108 View
  3. Akyol S. New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks. PeerJ Computer Science 2023;9:e1661 View
  4. Dalal S, Jain S, Dave M. Convolution Neural Network Having Multiple Channels with Own Attention Layer for Depression Detection from Social Data. New Generation Computing 2024;42(1):135 View
  5. Dalal S, Jain S, Dave M. An Investigation of Data Requirements for the Detection of Depression from Social Media Posts. Recent Patents on Engineering 2022;17(3) View
  6. Bertl M, Bignoumba N, Ross P, Yahia S, Draheim D. Evaluation of deep learning-based depression detection using medical claims data. Artificial Intelligence in Medicine 2024;147:102745 View
  7. Tiwari S, Pandey R, Deepak A, Singh J, Tripathi S. An ensemble approach to detect depression from social media platform: E-CLS. Multimedia Tools and Applications 2024;83(28):71001 View
  8. Mieles Toloza I, Delgado Meza J, Acevedo-Suárez J. Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios. Revista Politécnica 2024;53(1):57 View
  9. 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
  10. Mlakar I, Arioz U, Smrke U, Plohl N, Šafran V, Rojc M. An End-to-End framework for extracting observable cues of depression from diary recordings. Expert Systems with Applications 2024;257:125025 View
  11. Merayo N, Ayuso-Lanchares A, González-Sanguino C. Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram. PeerJ Computer Science 2024;10:e2251 View
  12. Chitale V, Henry J, Liang H, Matthews B, Baghaei N. Virtual reality analytics map (VRAM): A conceptual framework for detecting mental disorders using virtual reality data. New Ideas in Psychology 2025;76:101127 View
  13. Dalal S, Jain S, Dave M. DepressionFeature: Underlying ontology for user-specific depression analysis. The Journal of Supercomputing 2025;81(1) View

Books/Policy Documents

  1. Ahmad H, Nasir F, Faisal C, Ahmad S. Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media. View
  2. Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
  3. Shete M, Sardey C, Bhorge S. Intelligent Systems. View
  4. Asma S, Akhter N, Afrin M, Hasan S, Mia M, Ali K. Information, Communication and Computing Technology. View
  5. Gorrab A, Bonnerot T. Intelligent Systems and Applications. View
  6. Shangguan Z, Li X, Dong Y, Yuan X. Quality, Reliability, Security and Robustness in Heterogeneous Systems. View