Abstract
Depression is a common illness that negatively affects how people feel, think, and act. It causes feelings of sadness and sleeping disorders. In serious cases, it leads to self-harm or suicide. Many researchers in computer science addressed the problem of depression detection. However, less research concerns the emotional upheaval of depressive people and investigates the reasons behind the depression. In this paper, a deep learning model is first constructed to automatically determine the negative sentiment degree for a Facebook post. The curves of emotional upheavals for depressive users are then generated. Based on the post contents, weather, and news data, relevant events are detected to infer the reasons of the negative emotions. A correlation analysis between the behavioral data of the depressive users on Facebook and their negative emotions is also conducted. The results of this study can not only provide a self-examination tool for depressive people, but also serve as a diagnostic assessment reference for medical personnel.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the ACM Symposium on Applied Computing |
| Publisher | Association for Computing Machinery |
| Pages | 2086-2095 |
| Number of pages | 10 |
| ISBN (Print) | 9781450359337 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
| Event | 34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus Duration: 2019 Apr 8 → 2019 Apr 12 |
Publication series
| Name | Proceedings of the ACM Symposium on Applied Computing |
|---|---|
| Volume | Part F147772 |
Conference
| Conference | 34th Annual ACM Symposium on Applied Computing, SAC 2019 |
|---|---|
| Country | Cyprus |
| City | Limassol |
| Period | 19/4/8 → 19/4/12 |
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Keywords
- Depression
- Event detection
- Social media
- Text sentiment classification
ASJC Scopus subject areas
- Software
Cite this
Event detection for exploring emotional upheavals of depressive people. / Wu, Pin Hua; Koh, Jia-Ling; Chen, Arbee L.P.
Proceedings of the ACM Symposium on Applied Computing. Association for Computing Machinery, 2019. p. 2086-2095 (Proceedings of the ACM Symposium on Applied Computing; Vol. Part F147772).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Event detection for exploring emotional upheavals of depressive people
AU - Wu, Pin Hua
AU - Koh, Jia-Ling
AU - Chen, Arbee L.P.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Depression is a common illness that negatively affects how people feel, think, and act. It causes feelings of sadness and sleeping disorders. In serious cases, it leads to self-harm or suicide. Many researchers in computer science addressed the problem of depression detection. However, less research concerns the emotional upheaval of depressive people and investigates the reasons behind the depression. In this paper, a deep learning model is first constructed to automatically determine the negative sentiment degree for a Facebook post. The curves of emotional upheavals for depressive users are then generated. Based on the post contents, weather, and news data, relevant events are detected to infer the reasons of the negative emotions. A correlation analysis between the behavioral data of the depressive users on Facebook and their negative emotions is also conducted. The results of this study can not only provide a self-examination tool for depressive people, but also serve as a diagnostic assessment reference for medical personnel.
AB - Depression is a common illness that negatively affects how people feel, think, and act. It causes feelings of sadness and sleeping disorders. In serious cases, it leads to self-harm or suicide. Many researchers in computer science addressed the problem of depression detection. However, less research concerns the emotional upheaval of depressive people and investigates the reasons behind the depression. In this paper, a deep learning model is first constructed to automatically determine the negative sentiment degree for a Facebook post. The curves of emotional upheavals for depressive users are then generated. Based on the post contents, weather, and news data, relevant events are detected to infer the reasons of the negative emotions. A correlation analysis between the behavioral data of the depressive users on Facebook and their negative emotions is also conducted. The results of this study can not only provide a self-examination tool for depressive people, but also serve as a diagnostic assessment reference for medical personnel.
KW - Depression
KW - Event detection
KW - Social media
KW - Text sentiment classification
UR - http://www.scopus.com/inward/record.url?scp=85065639679&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065639679&partnerID=8YFLogxK
U2 - 10.1145/3297280.3297485
DO - 10.1145/3297280.3297485
M3 - Conference contribution
AN - SCOPUS:85065639679
SN - 9781450359337
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 2086
EP - 2095
BT - Proceedings of the ACM Symposium on Applied Computing
PB - Association for Computing Machinery
ER -