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

Theme issue 2019: Health Natural Language Processing (17) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1553) Natural Language Processing (637) Ontologies, Classifications, and Coding (224) Depression and Mood Disorders; Suicide Prevention (1272)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17650, first published .
Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

Authors of this article:

Genghao Li1 Author Orcid Image ;   Bing Li1 Author Orcid Image ;   Langlin Huang1 Author Orcid Image ;   Sibing Hou2 Author Orcid Image
Article Authors Cited by (18) Tweetations (1) Metrics

Journals

  1. Gupta S, Goel L, Singh A, Prasad A, Ullah M, Hošovský A. Psychological Analysis for Depression Detection from Social Networking Sites. Computational Intelligence and Neuroscience 2022;2022:1 View
  2. Khan J, Lee S. Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text. Applied Sciences 2021;11(17):8172 View
  3. Chang A, Xian X, Liu M, Zhao X. Health Communication through Positive and Solidarity Messages Amid the COVID-19 Pandemic: Automated Content Analysis of Facebook Uses. International Journal of Environmental Research and Public Health 2022;19(10):6159 View
  4. Rabie E, Hashem A, Alsheref F. Depression Detection Model in Social Network Content. International Journal of Software Innovation 2022;10(1):1 View
  5. Han Y, Pan W, Li J, Zhang T, Zhang Q, Zhang E. Developmental Trend of Subjective Well-Being of Weibo Users During COVID-19: Online Text Analysis Based on Machine Learning Method. Frontiers in Psychology 2022;12 View
  6. Zhou L, Liu Z, Yuan X, Shangguan Z, Li Y, Hu B. CAIINET: Neural network based on contextual attention and information interaction mechanism for depression detection. Digital Signal Processing 2023;137:103986 View
  7. 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
  8. Kim K, Kim J, Rhee H, Youn B. Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest. Scientific Reports 2023;13(1) View
  9. Li C, Fu J, Lai J, Sun L, Zhou C, Li W, Jian B, Deng S, Zhang Y, Guo Z, Liu Y, Zhou Y, Xie S, Hou M, Wang R, Chen Q, Wu Y. Construction of an Emotional Lexicon of Patients With Breast Cancer: Development and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e44897 View
  10. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  11. Du N, Wang Y, Huang Y. Parental Depression and Self-Stigma Among Chinese Young People Living With Depression: A Qualitative Study. Qualitative Health Research 2024 View
  12. Lau N, Zhao X, O'Daffer A, Weissman H, Barton K. Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis. JMIR Cancer 2024;10:e52061 View
  13. Chen J, Liu S, Xu M, Wang P. Enhancing depression detection: A multimodal approach with text extension and content fusion. Expert Systems 2024 View

Books/Policy Documents

  1. Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
  2. Selvaraj S, Selva Nidhyananthan S. Machine Learning Algorithms Using Scikit and TensorFlow Environments. View
  3. Lima Filho S, da Silva M, Oliveira J. Digital Humanities Looking at the World. View