Abstract
We develop a computational model to discover the potential causes of depression by analysing the topics from user-generated contents. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient’s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.
Original language | English |
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Title of host publication | Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017) |
Publisher | AFNLP |
Pages | 9-17 |
Number of pages | 9 |
ISBN (Print) | 978-1-948087-07-0 |
Publication status | Published - 27 Nov 2017 |
Event | The 8th International Joint Conference on Natural Language Processing - Taipei, Taiwan, Province of China Duration: 27 Nov 2017 → 1 Dec 2017 http://ijcnlp2017.org/site/page.aspx?pid=901&sid=1133&lang=en |
Conference
Conference | The 8th International Joint Conference on Natural Language Processing |
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Abbreviated title | IJCNLP 2017 |
Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 27/11/17 → 1/12/17 |
Internet address |