TY - GEN
T1 - Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task
AU - Preoţiuc-Pietro, Daniel
AU - Sap, Maarten
AU - Schwartz, H. Andrew
AU - Ungar, Lyle
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - This article is a system description and report on the submission of the World Well-Being Project from the University of Pennsylvania in the ‘CLPsych 2015’ shared task. The goal of the shared task was to automatically determine Twitter users who self-reported having one of two mental illnesses: post traumatic stress disorder (PTSD) and depression. Our system employs user metadata and textual features derived from Twitter posts. To reduce the feature space and avoid data sparsity, we consider several word clustering approaches. We explore the use of linear classifiers based on different feature sets as well as a combination use a linear ensemble. This method is agnostic of illness specific features, such as lists of medicines, thus making it readily applicable in other scenarios. Our approach ranked second in all tasks on average precision and showed best results at .1 false positive rates.
AB - This article is a system description and report on the submission of the World Well-Being Project from the University of Pennsylvania in the ‘CLPsych 2015’ shared task. The goal of the shared task was to automatically determine Twitter users who self-reported having one of two mental illnesses: post traumatic stress disorder (PTSD) and depression. Our system employs user metadata and textual features derived from Twitter posts. To reduce the feature space and avoid data sparsity, we consider several word clustering approaches. We explore the use of linear classifiers based on different feature sets as well as a combination use a linear ensemble. This method is agnostic of illness specific features, such as lists of medicines, thus making it readily applicable in other scenarios. Our approach ranked second in all tasks on average precision and showed best results at .1 false positive rates.
UR - https://www.scopus.com/pages/publications/85108566317
M3 - Conference contribution
AN - SCOPUS:85108566317
T3 - 2nd Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, CLPsych 2015 - Proceedings of the Workshop
SP - 40
EP - 45
BT - 2nd Computational Linguistics and Clinical Psychology
PB - Association for Computational Linguistics (ACL)
T2 - 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, CLPsych 2015
Y2 - 5 June 2015
ER -