Published on in Vol 4, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18246, first published .
Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count

Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count

Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count

Journals

  1. Ahmed U, Mukhiya S, Srivastava G, Lamo Y, Lin J. Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment. Frontiers in Psychology 2021;12 View
  2. Ahmed U, Lin J, Srivastava G. Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment. ACM Transactions on Asian and Low-Resource Language Information Processing 2022;21(5):1 View
  3. Ahmed U, Lin J, Srivastava G. Graph Attention Network for Text Classification and Detection of Mental Disorder. ACM Transactions on the Web 2023;17(3):1 View
  4. Ahmed U, Lin J, Srivastava G. Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. IEEE Journal of Biomedical and Health Informatics 2023;27(2):768 View
  5. Ahmed U, Srivastava G, Yun U, Lin J. EANDC: An explainable attention network based deep adaptive clustering model for mental health treatment. Future Generation Computer Systems 2022;130:106 View
  6. Lu S, Zhao L, Lai L, Shi C, Jiang W. How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media. International Journal of Environmental Research and Public Health 2022;19(3):1822 View
  7. Ahmed U, Lin J, Srivastava G. Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT. ACM Transactions on Sensor Networks 2023;19(3):1 View
  8. Villegas-Ch. W, Erazo D, Ortiz-Garces I, Gaibor-Naranjo W, Palacios-Pacheco X. Artificial Intelligence Model for the Identification of the Personality of Twitter Users through the Analysis of Their Behavior in the Social Network. Electronics 2022;11(22):3811 View
  9. Maletta R, Vass V. A 20-year review comparing the use of ‘schizophrenia’ and ‘psychosis’ in UK newspapers from 2000 to 2019: Implications for stigma reduction. Schizophrenia Research 2023;251:66 View
  10. Watkins M, Mallion J, Frings D, Wills J, Sykes S, Whittaker A. Public health messages during a global emergency through an online community: a discourse and sentiment analysis. Frontiers in Digital Health 2023;5 View
  11. Hui V, Eby M, Constantino R, Lee H, Zelazny J, Chang J, He D, Lee Y. Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach. Journal of Medical Internet Research 2023;25:e48607 View
  12. du Plessis C. Emotional brand communication on social media to foster financial well-being. Online Journal of Communication and Media Technologies 2023;13(4):e202342 View
  13. Sun S, Plate R, Jones C, Rodriguez Y, Katz C, Murin M, Pearson J, Parish-Morris J, Waller R. Childhood conduct problems and parent–child talk during social and nonsocial play contexts: a naturalistic home-based experiment. Scientific Reports 2024;14(1) View
  14. Trifu R, Nemeș B, Herta D, Bodea-Hategan C, Talaș D, Coman H. Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure. Frontiers in Psychology 2024;15 View
  15. García Y, Villa-Pérez M, Li K, Tai X, Trejo L, Daza-Torres M, Montesinos-López J, Nuño M. Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter. Frontiers in Public Health 2024;12 View

Books/Policy Documents

  1. Ahmed U, Lin J, Srivastava G. Advances in Knowledge Discovery and Data Mining. View
  2. Denecke K. Sentiment Analysis in the Medical Domain. View