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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| 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 |
| _version_ | 1796996196222369792 |
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| author | Lim, Shi Ru |
| author_facet | Lim, Shi Ru |
| author_sort | Lim, Shi Ru |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T13:13:24Z |
| format | Undergraduates Project Papers |
| id | UMPir40166 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:13:24Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | UMPir401662024-02-07T03:46:30Z 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. |
| spellingShingle | QA75 Electronic computers. Computer science Lim, Shi Ru Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques |
| title | 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_short | Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques |
| title_sort | predicting mental health disorder on twitter using machine learning techniques |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/40166/1/CA19033.pdf |
| work_keys_str_mv | AT limshiru predictingmentalhealthdisorderontwitterusingmachinelearningtechniques |