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

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1771) Infodemiology and Infoveillance (1443) Depression and Mood Disorders; Suicide Prevention (1499) Ontologies, Classifications, and Coding (267) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1085)

Published on in Vol 19, No 7 (2017): July

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Authors of this article:

Hyesil Jung1 Author Orcid Image ;   Hyeoun-Ae Park1 Author Orcid Image ;   Tae-Min Song2 Author Orcid Image
Article Authors Cited by (43) Tweetations (8) Metrics

Journals

  1. Zheng Z, Yang Q, Liu Z, Qiu J, Gu J, Hao Y, Song C, Jia Z, Hao C. Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data. Journal of Medical Internet Research 2020;22(1):e13201 View
  2. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  3. On J, Park H, Song T. Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology. Journal of Medical Internet Research 2019;21(6):e13456 View
  4. Hipson W. Using sentiment analysis to detect affect in children’s and adolescents’ poetry. International Journal of Behavioral Development 2019;43(4):375 View
  5. Jing X, Hardiker N, Kay S, Gao Y. Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach. JMIR Medical Informatics 2018;6(4):e52 View
  6. Giuntini F, Cazzolato M, dos Reis M, Campbell A, Traina A, Ueyama J. A review on recognizing depression in social networks: challenges and opportunities. Journal of Ambient Intelligence and Humanized Computing 2020;11(11):4713 View
  7. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  8. Song J, Han Y, Kim K, Song T. Social big data analysis of future signals for bullying in South Korea: Application of general strain theory. Telematics and Informatics 2020;54:101472 View
  9. Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. Journal of Medical Systems 2020;44(12) View
  10. Lee J, Lee J, Park S. An Ontology of Intimate Partnerships for Social Big Data Research. Journal of Families and Better Life 2020;38(4):1 View
  11. Rassy J, Bardon C, Dargis L, Côté L, Corthésy-Blondin L, Mörch C, Labelle R. Information and Communication Technology Use in Suicide Prevention: Scoping Review. Journal of Medical Internet Research 2021;23(5):e25288 View
  12. Dragos V, Battistelli D, Kellodjoue E. A formal representation of appraisal categories for social data analysis. Procedia Computer Science 2020;176:928 View
  13. Lee J, Park H, Park S, Song T. Using Social Media Data to Understand Consumers' Information Needs and Emotions Regarding Cancer: Ontology-Based Data Analysis Study. Journal of Medical Internet Research 2020;22(12):e18767 View
  14. Chiong R, Budhi G, Dhakal S, Chiong F. A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Computers in Biology and Medicine 2021;135:104499 View
  15. Lee J, Park H, Song T. A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology. Journal of Medical Internet Research 2021;23(6):e25028 View
  16. Arias F, Zambrano Nunez M, Guerra-Adames A, Tejedor-Flores N, Vargas-Lombardo M. Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access 2022;10:74850 View
  17. Lokala U, Lamy F, Daniulaityte R, Gaur M, Gyrard A, Thirunarayan K, Kursuncu U, Sheth A. Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study. JMIR Public Health and Surveillance 2022;8(12):e24938 View
  18. Dias L, Vianna H, Barbosa J. Human behaviour data analysis and noncommunicable diseases: a systematic mapping study. Behaviour & Information Technology 2023;42(14):2485 View
  19. Guo J, Keeshin B, Conway M, Chapman W, Sward K. A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People. The Journal of School Nursing 2022;38(1):74 View
  20. Mullick T, Radovic A, Shaaban S, Doryab A. Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study. JMIR Formative Research 2022;6(6):e35807 View
  21. Yang S, Huang P, Li B, Gan T, Lin W, Liu Y. The relationship of negative life events, trait-anxiety and depression among Chinese university students: A moderated effect of self-esteem. Journal of Affective Disorders 2023;339:384 View
  22. Królak A, Wiktorski T, Żmudzińska A. Automatic analysis of X (Twitter) data for supporting depression diagnosis. Human Technology 2023;19(3):370 View
  23. 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
  24. Zhang W, Xie J, Zhang Z, Liu X. Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach. Journal of Management Information Systems 2024;41(2):546 View
  25. Dalal S, Jain S, Dave M. DepressionFeature: Underlying ontology for user-specific depression analysis. The Journal of Supercomputing 2025;81(1) View
  26. Ma H, Shen L, Wang J, Wang S, Wang M, Wang M, Li Z, Li J. Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology. Database 2024;2024 View
  27. Ismail O. Born-Digital Memes as Archival Discourse: A Linked-Data Analysis of Cultural Sentiment and Polarization. Journalism and Media 2025;6(1):28 View

Books/Policy Documents

  1. Wang D, Xu L, Younas A. Machine Learning and Data Mining in Pattern Recognition. View
  2. López-Martínez A, García-Díaz J, Valencia-García R, Ruiz-Martínez A. Technologies and Innovation. View
  3. Singla S. Ontology‐Based Information Retrieval for Healthcare Systems. View
  4. Bhatia S, Kesarwani Y, Basantani A, Jain S. Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. View
  5. Bandyopadhyay A, Shenoy K. Advances in Information and Communication. View
  6. Verma A, Harper M, Assi S, Al-Hamid A, Yousif M, Mustafina J, Ismail N, Al-Jumeily OBE D. Data Science and Emerging Technologies. View
  7. Jain S, Dalal S, Dave M. Semantic Intelligence. View
  8. Symeonaki M, Hyggen C, Parsanoglou D, Mifsud L, Stamou G. Understanding The Everyday Digital Lives of Children and Young People. View
  9. . Data Analysis and Related Applications 3. View
  10. Ortiz E, Barrero J, Castro-Osorio R, Domínguez A, Caicedo N. Advances in Computing. View

Conference Proceedings

  1. Du J, Zhang Y, Tao C, Xu H. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). A pilot study of mining association between psychiatric stressors and symptoms in tweets View
  2. Kardinata E, Rakhmawati N, Zuhroh N. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT). Ontology-Based Sentiment Analysis on News Title View
  3. Razdan A, S S. 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV). Hate Speech Detection using ML algorithms View
  4. Zerrouki K, Hamou R, Rahmoun A. 2021 International Conference on Information Systems and Advanced Technologies (ICISAT). A novel bio-inspired approach based on Spotted Hyenas for Suicide Prediction View
  5. Zulkarnain N, Abd Yusof N, Ahmad S, Othman Z, Hashim A. 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). Performance of Content-Based Features to Detect Depression Tendencies in Different Text Lengths View
  6. Shah K, Patel U, Kumar Y. 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). Machine Learning-Based Approaches for Early Prediction of Depression View