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An optimized deep learning approach for suicide detection through Arabic tweets
Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455273/ https://www.ncbi.nlm.nih.gov/pubmed/36092010 http://dx.doi.org/10.7717/peerj-cs.1070 |
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author | Baghdadi, Nadiah A. Malki, Amer Magdy Balaha, Hossam AbdulAzeem, Yousry Badawy, Mahmoud Elhosseini, Mostafa |
author_facet | Baghdadi, Nadiah A. Malki, Amer Magdy Balaha, Hossam AbdulAzeem, Yousry Badawy, Mahmoud Elhosseini, Mostafa |
author_sort | Baghdadi, Nadiah A. |
collection | PubMed |
description | Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%. |
format | Online Article Text |
id | pubmed-9455273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94552732022-09-09 An optimized deep learning approach for suicide detection through Arabic tweets Baghdadi, Nadiah A. Malki, Amer Magdy Balaha, Hossam AbdulAzeem, Yousry Badawy, Mahmoud Elhosseini, Mostafa PeerJ Comput Sci Computational Linguistics Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%. PeerJ Inc. 2022-08-23 /pmc/articles/PMC9455273/ /pubmed/36092010 http://dx.doi.org/10.7717/peerj-cs.1070 Text en © 2022 Baghdadi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Computational Linguistics Baghdadi, Nadiah A. Malki, Amer Magdy Balaha, Hossam AbdulAzeem, Yousry Badawy, Mahmoud Elhosseini, Mostafa An optimized deep learning approach for suicide detection through Arabic tweets |
title | An optimized deep learning approach for suicide detection through Arabic tweets |
title_full | An optimized deep learning approach for suicide detection through Arabic tweets |
title_fullStr | An optimized deep learning approach for suicide detection through Arabic tweets |
title_full_unstemmed | An optimized deep learning approach for suicide detection through Arabic tweets |
title_short | An optimized deep learning approach for suicide detection through Arabic tweets |
title_sort | optimized deep learning approach for suicide detection through arabic tweets |
topic | Computational Linguistics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455273/ https://www.ncbi.nlm.nih.gov/pubmed/36092010 http://dx.doi.org/10.7717/peerj-cs.1070 |
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