Deep learning based mental health/status interpretation
Detecting mental health disorders through analysis of social media activity is a challenging yet crucial task, particularly in terms of early intervention for individuals experiencing mental health issues. This study introduces an approach to interpreting mental health conditions by employing Deep L...
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Nanyang Technological University
2023
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sg-ntu-dr.10356-1719652023-11-24T15:38:14Z Deep learning based mental health/status interpretation Teo, Guang Xiang Vidya Sudarshan School of Computer Science and Engineering vidya.sudarshan@ntu.edu.sg Engineering::Computer science and engineering Detecting mental health disorders through analysis of social media activity is a challenging yet crucial task, particularly in terms of early intervention for individuals experiencing mental health issues. This study introduces an approach to interpreting mental health conditions by employing Deep Learning models on Reddit posts. The research utilized deep learning models to examine and classify posts that are associated with mental health disorders. The primary dataset consisted of Reddit posts, with a specific focus on identifying posts related to depression. Moreover, the dataset was also utilized to categorize posts pertaining to various other mental disorders. The study implemented a two-stage classifier to facilitate effective analysis. The initial stage involved filtering out posts that were not relevant to mental disorders, while the subsequent stage focused on categorizing the remaining posts into specific mental disorders. This innovative two- stage approach offers a fresh perspective on utilizing social media data for mental health analysis and has the potential to make significant contributions to the field of digital mental health. Bachelor of Engineering (Computer Engineering) 2023-11-20T00:14:09Z 2023-11-20T00:14:09Z 2023 Final Year Project (FYP) Teo, G. X. (2023). Deep learning based mental health/status interpretation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171965 https://hdl.handle.net/10356/171965 en SCSE22-1073 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Teo, Guang Xiang Deep learning based mental health/status interpretation |
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Detecting mental health disorders through analysis of social media activity is a challenging yet crucial task, particularly in terms of early intervention for individuals experiencing mental health issues. This study introduces an approach to interpreting mental health conditions by employing Deep Learning models on Reddit posts. The research utilized deep learning models to examine and classify posts that are associated with mental health disorders. The primary dataset consisted of Reddit posts, with a specific focus on identifying posts related to depression. Moreover, the dataset was also utilized to categorize posts pertaining to various other mental disorders. The study implemented a two-stage classifier to facilitate effective analysis. The initial stage involved filtering out posts that were not relevant to mental disorders, while the subsequent stage focused on categorizing the remaining posts into specific mental disorders. This innovative two- stage approach offers a fresh perspective on utilizing social media data for mental health analysis and has the potential to make significant contributions to the field of digital mental health. |
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Vidya Sudarshan |
author_facet |
Vidya Sudarshan Teo, Guang Xiang |
format |
Final Year Project |
author |
Teo, Guang Xiang |
author_sort |
Teo, Guang Xiang |
title |
Deep learning based mental health/status interpretation |
title_short |
Deep learning based mental health/status interpretation |
title_full |
Deep learning based mental health/status interpretation |
title_fullStr |
Deep learning based mental health/status interpretation |
title_full_unstemmed |
Deep learning based mental health/status interpretation |
title_sort |
deep learning based mental health/status interpretation |
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Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171965 |
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1783955642323566592 |