Recognition model for major depressive disorder in Arabic user-generated content
Abstract Background One of the psychological problems that have become very prevalent in the modern world is depression, where mental health disorders have become very common. Depression, as reported by the WHO, is the second-largest factor in the worldwide burden of illnesses. As these issues grow,...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Beni-Suef University Journal of Basic and Applied Sciences |
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| Online Access: | https://doi.org/10.1186/s43088-024-00592-9 |
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| author | Esraa M. Rabie Atef F. Hashem Fahad Kamal Alsheref |
| author_facet | Esraa M. Rabie Atef F. Hashem Fahad Kamal Alsheref |
| author_sort | Esraa M. Rabie |
| collection | DOAJ |
| description | Abstract Background One of the psychological problems that have become very prevalent in the modern world is depression, where mental health disorders have become very common. Depression, as reported by the WHO, is the second-largest factor in the worldwide burden of illnesses. As these issues grow, social media has become a tremendous platform for people to express themselves. A user’s social media behavior may therefore disclose a lot about their emotional state and mental health. This research offers a novel framework for depression detection from Arabic textual data utilizing deep learning (DL), natural language processing (NLP), machine learning (ML), and BERT transformers techniques in light of the disease’s high prevalence. To do this, a dataset of tweets was used, which was collected from 3 sources, as we mention later. The dataset was constructed in two variants, one with binary classification and the other with multi-classification. Results In binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” In comparison ML with BERT transformers, ARABERT has high accuracy in binary classification with a 93.03 percent accuracy rate. In multi-classification, we used DL techniques such as “long short-term memory (LSTM),” and used BERT transformers “Multilingual BERT.” In comparison DL with BERT transformers, multilingual has high accuracy in multi-classification with an accuracy of 97.8%. Conclusion Through user-generated content, we can detect depressed people using artificial intelligence technology in a fast manner and with high accuracy instead of medical technology. |
| format | Article |
| id | doaj-art-38c3089a1eb8477e808c1ad8b728816d |
| institution | Kabale University |
| issn | 2314-8543 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Beni-Suef University Journal of Basic and Applied Sciences |
| spelling | doaj-art-38c3089a1eb8477e808c1ad8b728816d2025-01-26T12:47:40ZengSpringerOpenBeni-Suef University Journal of Basic and Applied Sciences2314-85432025-01-0114111610.1186/s43088-024-00592-9Recognition model for major depressive disorder in Arabic user-generated contentEsraa M. Rabie0Atef F. Hashem1Fahad Kamal Alsheref2Mathematics and Computer Science Department, Faculty of Science, Beni-Suef UniversityMathematics and Computer Science Department, Faculty of Science, Beni-Suef UniversityInformation Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef UniversityAbstract Background One of the psychological problems that have become very prevalent in the modern world is depression, where mental health disorders have become very common. Depression, as reported by the WHO, is the second-largest factor in the worldwide burden of illnesses. As these issues grow, social media has become a tremendous platform for people to express themselves. A user’s social media behavior may therefore disclose a lot about their emotional state and mental health. This research offers a novel framework for depression detection from Arabic textual data utilizing deep learning (DL), natural language processing (NLP), machine learning (ML), and BERT transformers techniques in light of the disease’s high prevalence. To do this, a dataset of tweets was used, which was collected from 3 sources, as we mention later. The dataset was constructed in two variants, one with binary classification and the other with multi-classification. Results In binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” In comparison ML with BERT transformers, ARABERT has high accuracy in binary classification with a 93.03 percent accuracy rate. In multi-classification, we used DL techniques such as “long short-term memory (LSTM),” and used BERT transformers “Multilingual BERT.” In comparison DL with BERT transformers, multilingual has high accuracy in multi-classification with an accuracy of 97.8%. Conclusion Through user-generated content, we can detect depressed people using artificial intelligence technology in a fast manner and with high accuracy instead of medical technology.https://doi.org/10.1186/s43088-024-00592-9DepressionClassificationTweetsMachine learningBERT transformersDeep learning |
| spellingShingle | Esraa M. Rabie Atef F. Hashem Fahad Kamal Alsheref Recognition model for major depressive disorder in Arabic user-generated content Beni-Suef University Journal of Basic and Applied Sciences Depression Classification Tweets Machine learning BERT transformers Deep learning |
| title | Recognition model for major depressive disorder in Arabic user-generated content |
| title_full | Recognition model for major depressive disorder in Arabic user-generated content |
| title_fullStr | Recognition model for major depressive disorder in Arabic user-generated content |
| title_full_unstemmed | Recognition model for major depressive disorder in Arabic user-generated content |
| title_short | Recognition model for major depressive disorder in Arabic user-generated content |
| title_sort | recognition model for major depressive disorder in arabic user generated content |
| topic | Depression Classification Tweets Machine learning BERT transformers Deep learning |
| url | https://doi.org/10.1186/s43088-024-00592-9 |
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