Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the CLPysch 2018 Shared Task

Sharath Chandra Guntuku, Salvatore Giorgi, Lyle Ungar

Abstract
This article is a system description and report on the submission of a team from the University of Pennsylvania in the ’CLPsych 2018’ shared task. The goal of the shared task was to use childhood language as a marker for both current and future psychological health over individual lifetimes. Our system employs multiple textual features derived from the essays written and individuals’ socio-demographic variables at the age of 11. We considered several word clustering approaches, and explore the use of linear regression based on different feature sets. Our approach showed best results for predicting distress at the age of 42 and for predicting current anxiety on Disattenuated Pearson Correlation, and ranked fourth in the future health prediction task. In addition to the subtasks presented, we attempted to provide insight into mental health aspects at different ages. Our findings indicate that misspellings, words with illegible letters and increased use of personal pronouns are correlated with poor mental health at age 11, while descriptions about future physical activity, family and friends are correlated with good mental health.
Anthology ID:
W18-0610
Volume:
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
Month:
June
Year:
2018
Address:
New Orleans, LA
Editors:
Kate Loveys, Kate Niederhoffer, Emily Prud’hommeaux, Rebecca Resnik, Philip Resnik
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–106
Language:
URL:
https://aclanthology.org/W18-0610
DOI:
10.18653/v1/W18-0610
Bibkey:
Cite (ACL):
Sharath Chandra Guntuku, Salvatore Giorgi, and Lyle Ungar. 2018. Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the CLPysch 2018 Shared Task. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 98–106, New Orleans, LA. Association for Computational Linguistics.
Cite (Informal):
Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the CLPysch 2018 Shared Task (Guntuku et al., CLPsych 2018)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/W18-0610.pdf
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