Towards Capturing Changes in Mood and Identifying Suicidality Risk

Sravani Boinepelli, Shivansh Subramanian, Abhijeeth Singam, Tathagata Raha, Vasudeva Varma


Abstract
This paper describes our systems for CLPsych?s 2022 Shared Task. Subtask A involves capturing moments of change in an individual?s mood over time, while Subtask B asked us to identify the suicidality risk of a user. We explore multiple machine learning and deep learning methods for the same, taking real-life applicability into account while considering the design of the architecture. Our team achieved top results in different categories for both subtasks. Task A was evaluated on a post-level (using macro averaged F1) and on a window-based timeline level (using macro-averaged precision and recall). We scored a post-level F1 of 0.520 and ranked second with a timeline-level recall of 0.646. Task B was a user-level task where we also came in second with a micro F1 of 0.520 and scored third place on the leaderboard with a macro F1 of 0.380.
Anthology ID:
2022.clpsych-1.24
Volume:
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
Month:
July
Year:
2022
Address:
Seattle, USA
Editors:
Ayah Zirikly, Dana Atzil-Slonim, Maria Liakata, Steven Bedrick, Bart Desmet, Molly Ireland, Andrew Lee, Sean MacAvaney, Matthew Purver, Rebecca Resnik, Andrew Yates
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
245–250
Language:
URL:
https://aclanthology.org/2022.clpsych-1.24
DOI:
10.18653/v1/2022.clpsych-1.24
Bibkey:
Cite (ACL):
Sravani Boinepelli, Shivansh Subramanian, Abhijeeth Singam, Tathagata Raha, and Vasudeva Varma. 2022. Towards Capturing Changes in Mood and Identifying Suicidality Risk. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 245–250, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Towards Capturing Changes in Mood and Identifying Suicidality Risk (Boinepelli et al., CLPsych 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.clpsych-1.24.pdf