Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin
According to the research conducted by the World Health Organization (WHO) in 2015, approximately 300 million of people around the globe are suffering with depression. The research also shows that there is an increase of 18% in the number depression cases diagnosed between 2007 and 2015. Depression...
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| Format: | Thesis |
| Language: | English |
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2020
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| Online Access: | https://ir.uitm.edu.my/id/eprint/31570/ |
| _version_ | 1848807794502795264 |
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| author | Kamaruddin, Nur Amalina |
| author_facet | Kamaruddin, Nur Amalina |
| author_sort | Kamaruddin, Nur Amalina |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | According to the research conducted by the World Health Organization (WHO) in 2015, approximately 300 million of people around the globe are suffering with depression. The research also shows that there is an increase of 18% in the number depression cases diagnosed between 2007 and 2015. Depression is defined as a mental disorder that leads to constant feeling of sadness and also disintegration of interest in an activity that an individual used to enjoy. It also contributes to the inability to carry out daily activities (WHO, 2015). Thus, a Depression Prediction System was developed to predict depression from tweets. The main function of this system is to classify tweet into “depressed” and “not depressed”. The classification model was built using Naïve Bayes algorithm. The number of data used in this project is 15952 with 1 independent variable and 1 dependent variables. These data in term of tweets need to go through data cleaning and data transformation before it can be processed by the classification model. Once the data has been transformed, it is divided into 80% to be used training data and the remaining 20% as testing data. |
| first_indexed | 2025-11-14T22:48:29Z |
| format | Thesis |
| id | uitm-31570 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T22:48:29Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-315702020-06-26T03:36:41Z https://ir.uitm.edu.my/id/eprint/31570/ Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin Kamaruddin, Nur Amalina Twitter Multivariate analysis. Cluster analysis. Longitudinal method Analysis According to the research conducted by the World Health Organization (WHO) in 2015, approximately 300 million of people around the globe are suffering with depression. The research also shows that there is an increase of 18% in the number depression cases diagnosed between 2007 and 2015. Depression is defined as a mental disorder that leads to constant feeling of sadness and also disintegration of interest in an activity that an individual used to enjoy. It also contributes to the inability to carry out daily activities (WHO, 2015). Thus, a Depression Prediction System was developed to predict depression from tweets. The main function of this system is to classify tweet into “depressed” and “not depressed”. The classification model was built using Naïve Bayes algorithm. The number of data used in this project is 15952 with 1 independent variable and 1 dependent variables. These data in term of tweets need to go through data cleaning and data transformation before it can be processed by the classification model. Once the data has been transformed, it is divided into 80% to be used training data and the remaining 20% as testing data. 2020 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/31570/3/31570.pdf Kamaruddin, Nur Amalina (2020) Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin. (2020) Degree thesis, thesis, Universiti Teknologi MARA, Cawangan Melaka. <http://terminalib.uitm.edu.my/31570.pdf> |
| spellingShingle | Twitter Multivariate analysis. Cluster analysis. Longitudinal method Analysis Kamaruddin, Nur Amalina Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin |
| title | Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin |
| title_full | Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin |
| title_fullStr | Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin |
| title_full_unstemmed | Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin |
| title_short | Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin |
| title_sort | depression prediction system from twitter’s tweet by using sentiment analysis / nur amalina kamaruddin |
| topic | Twitter Multivariate analysis. Cluster analysis. Longitudinal method Analysis |
| url | https://ir.uitm.edu.my/id/eprint/31570/ |