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

Natural Language Processing (636) Information Seeking, Information Needs (419) Machine Learning (1370) Obesity and Nutrition as Public Health Problem (277) Clinical Informatics (904)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34492, first published .
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

Authors of this article:

José Alberto Benítez-Andrades1 Author Orcid Image ;   José-Manuel Alija-Pérez2 Author Orcid Image ;   Maria-Esther Vidal3 Author Orcid Image ;   Rafael Pastor-Vargas4 Author Orcid Image ;   María Teresa García-Ordás2 Author Orcid Image
Article Authors Cited by (18) Tweetations (15) Metrics

Journals

  1. Fardouly J, Crosby R, Sukunesan S. Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions. Journal of Eating Disorders 2022;10(1) View
  2. Sauvayre R, Vernier J, Chauvière C. An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach. JMIR Medical Informatics 2022;10(5):e37831 View
  3. Messan K, Sulima P, Ghosh D, Nye J. The research foundation for COVID-19 vaccine development. Frontiers in Research Metrics and Analytics 2023;8 View
  4. White B, Gombert A, Nguyen T, Yau B, Ishizumi A, Kirchner L, León A, Wilson H, Jaramillo-Gutierrez G, Cerquides J, D’Agostino M, Salvi C, Sreenath R, Rambaud K, Samhouri D, Briand S, Purnat T. Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study. JMIR Infodemiology 2023;3:e47317 View
  5. Laison E, Hamza Ibrahim M, Boligarla S, Li J, Mahadevan R, Ng A, Muthuramalingam V, Lee W, Yin Y, Nasri B. Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis. Journal of Medical Internet Research 2023;25:e47014 View
  6. Hassan E, Abd El-Hafeez T, Shams M. Optimizing classification of diseases through language model analysis of symptoms. Scientific Reports 2024;14(1) View
  7. Nayak G, Alam W, Singh K, Avinash G, Ray M, Kumar R. Modelling monthly rainfall of India through transformer-based deep learning architecture. Modeling Earth Systems and Environment 2024;10(3):3119 View
  8. Pourkeyvan A, Safa R, Sorourkhah A. Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks. IEEE Access 2024;12:28025 View
  9. Rubio-Martín S, García-Ordás M, Bayón-Gutiérrez M, Prieto-Fernández N, Benítez-Andrades J. Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing. Health Information Science and Systems 2024;12(1) View
  10. Merhbene G, Puttick A, Kurpicz-Briki M. Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review. Frontiers in Psychiatry 2024;15 View
  11. Nunez J, Leung B, Ho C, Ng R, Bates A. Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing. Communications Medicine 2024;4(1) View
  12. Norris M, Obeid N, El‐Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. International Journal of Eating Disorders 2024;57(6):1357 View
  13. Li X, Liu T, Zhang L, Alqahtani F, Tolba A. A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context. IEEE Access 2024;12:55757 View
  14. Alawi A, Bozkurt F. A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data. Decision Analytics Journal 2024;11:100473 View
  15. Alharbi K, Haq M. Enhancing Disaster Response and Public Safety with Advanced Social Media Analytics and Natural Language Processing. Engineering, Technology & Applied Science Research 2024;14(3):14212 View
  16. Ghosh S, Burger P, Simeunovic M, Maas J, Petkovic M. Review of Machine Learning solutions for Eating Disorders. International Journal of Medical Informatics 2024:105526 View

Books/Policy Documents

  1. Rubio-Martí­n S, Garcí­a-Ordás M, Bayón-Gutiérrez M, Martí­nez Villamea S, Arias-Ramos N, Bení­tez-Andrades J. Global Challenges for a Sustainable Society. View