TY - GEN
T1 - 部落客憂鬱傾向分析與預測
AU - Tung, Chia Ming
AU - Lu, Wen Hsiang
N1 - Publisher Copyright:
© Proceedings of the 27th Conference on Computational Linguistics and Speech Processing, ROCLING 2015.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - According to the investigation report of the Department of Health, Executive Yuan, R.O.C. in 2007, it is estimated that about 7.3% of Taiwan's population suffer from the major depressive disorder. How to identify patients with depression tendency is one of important health issues. Thus, this project tries to develop a novel technique to automatically identify the depression tendency of bloggers using their blog posts. With the fast growth of social networks, bloggers usually write daily posts with their emotion and events happened in work, home, or life. Although there are lots of research works about emotion analysis and classification, to our knowledge, there is no work focusing on prediction of blogger's depression tendency based on emotion analysis. In this project, we try to analyze key factors affecting major depressive disorder, such as negative event, negative emotion, symptom and negative thought, and then use these four factors to assist bloggers to predict depression tendency. Therefore, we focus on the investigation of the following two research issues (1) analysis of relevant factors of depression on blog posts written by patients with the major depressive disorder, (2) development of event-emotion-driven depression tendency prediction model.
AB - According to the investigation report of the Department of Health, Executive Yuan, R.O.C. in 2007, it is estimated that about 7.3% of Taiwan's population suffer from the major depressive disorder. How to identify patients with depression tendency is one of important health issues. Thus, this project tries to develop a novel technique to automatically identify the depression tendency of bloggers using their blog posts. With the fast growth of social networks, bloggers usually write daily posts with their emotion and events happened in work, home, or life. Although there are lots of research works about emotion analysis and classification, to our knowledge, there is no work focusing on prediction of blogger's depression tendency based on emotion analysis. In this project, we try to analyze key factors affecting major depressive disorder, such as negative event, negative emotion, symptom and negative thought, and then use these four factors to assist bloggers to predict depression tendency. Therefore, we focus on the investigation of the following two research issues (1) analysis of relevant factors of depression on blog posts written by patients with the major depressive disorder, (2) development of event-emotion-driven depression tendency prediction model.
UR - http://www.scopus.com/inward/record.url?scp=85040227414&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040227414&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85040227414
T3 - Proceedings of the 27th Conference on Computational Linguistics and Speech Processing, ROCLING 2015
SP - 263
EP - 276
BT - Proceedings of the 27th Conference on Computational Linguistics and Speech Processing, ROCLING 2015
A2 - Chen, Sin-Horng
A2 - Wang, Hsin-Min
A2 - Chien, Jen-Tzung
A2 - Kao, Hung-Yu
A2 - Chang, Wen-Whei
A2 - Wang, Yih-Ru
A2 - Wu, Shih-Hung
PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
T2 - 27th Conference on Computational Linguistics and Speech Processing, ROCLING 2015
Y2 - 1 October 2015 through 2 October 2015
ER -