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Deep Learning for Depression Detection Using Twitter Data

  • Published:2023 Issue:2 Volume:36 Page:1301-1313
  • ISSN:1079-8587
  • Container-title:Intelligent Automation & Soft Computing
  • language:en
  • Short-container-title:

Author:

Sami Khafaga Doaa,Auvdaiappan Maheshwari,Deepa K.,Abouhawwash Mohamed,Khalid Karim Faten

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software

Reference28 articles.

1. Deep learning for depression detection from textual data;Amanat;Electronics,2022

2. Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media;Zogan;World Wide Web,2022

3. A feature selection approach for fall detection using various machine learning classifiers;Le;IEEE Access,2021

4. A hierarchical depression detection model based on vocal and emotional cues;Dong;Neurocomputing,2021

5. Automated cognitive health assessment in smart homes using machine learning;Javed;Sustainable Cities and Society,2021

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1. A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges;Computer Science Review;2024-08

2. The use of machine learning and deep learning models in detecting depression on social media: A systematic literature review;Personalized Medicine in Psychiatry;2024-07

3. Machine Learning-Based Approaches for Early Prediction of Depression;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

4. Depression detection for twitter users using sentiment analysis in English and Arabic tweets;Artificial Intelligence in Medicine;2024-01

5. Detecting Signs of Depression from Social Media Texts using Generalized Autoregressive Pretraining Transformer Model;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13
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