Abstract
This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.- Anthology ID:
- W19-3024
- Volume:
- Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Kate Niederhoffer, Kristy Hollingshead, Philip Resnik, Rebecca Resnik, Kate Loveys
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 182–187
- Language:
- URL:
- https://aclanthology.org/W19-3024
- DOI:
- 10.18653/v1/W19-3024
- Cite (ACL):
- Kristen Allen, Shrey Bagroy, Alex Davis, and Tamar Krishnamurti. 2019. ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 182–187, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit (Allen et al., CLPsych 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-3024.pdf