Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques

Social media gives young people a place to voice their difficulties and trade opinions on current events in the digital era. Therefore, it is possible to analyze human behaviour using internet media. However, the illness of mental disorder is common yet often ignored. Social media makes it possible...

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Main Author: Lim, Shi Ru
Format: Undergraduates Project Papers
Language:English
Published: 2022
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Online Access:http://umpir.ump.edu.my/id/eprint/40166/1/CA19033.pdf
http://umpir.ump.edu.my/id/eprint/40166/
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spelling my.ump.umpir.401662024-02-07T03:46:30Z http://umpir.ump.edu.my/id/eprint/40166/ Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques Lim, Shi Ru QA75 Electronic computers. Computer science Social media gives young people a place to voice their difficulties and trade opinions on current events in the digital era. Therefore, it is possible to analyze human behaviour using internet media. However, the illness of mental disorder is common yet often ignored. Social media makes it possible to identify mental health disorders in large populations. Many efforts have been made to evaluate individual postings using machine learning techniques to identify people with mental health conditions on social media. This study attempted to predict mental health disorders among Twitter users using machine learning techniques. Support Vector Machine (SVM), Decision Tree, and Nave Bayes are three examples of machine learning approaches applied in this study. To assess the algorithms, the performance and accuracy of these three algorithms are compared. 2022-12 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40166/1/CA19033.pdf Lim, Shi Ru (2022) Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah.
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Lim, Shi Ru
Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques
description Social media gives young people a place to voice their difficulties and trade opinions on current events in the digital era. Therefore, it is possible to analyze human behaviour using internet media. However, the illness of mental disorder is common yet often ignored. Social media makes it possible to identify mental health disorders in large populations. Many efforts have been made to evaluate individual postings using machine learning techniques to identify people with mental health conditions on social media. This study attempted to predict mental health disorders among Twitter users using machine learning techniques. Support Vector Machine (SVM), Decision Tree, and Nave Bayes are three examples of machine learning approaches applied in this study. To assess the algorithms, the performance and accuracy of these three algorithms are compared.
format Undergraduates Project Papers
author Lim, Shi Ru
author_facet Lim, Shi Ru
author_sort Lim, Shi Ru
title Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques
title_short Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques
title_full Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques
title_fullStr Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques
title_full_unstemmed Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques
title_sort predicting mental health disorder on twitter using machine learning techniques
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/40166/1/CA19033.pdf
http://umpir.ump.edu.my/id/eprint/40166/
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score 13.23648