Abstract
In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.- Anthology ID:
- 2022.ltedi-1.35
- Volume:
- Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
- Venue:
- LTEDI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 245–250
- Language:
- URL:
- https://aclanthology.org/2022.ltedi-1.35
- DOI:
- 10.18653/v1/2022.ltedi-1.35
- Cite (ACL):
- Manex Agirrezabal and Janek Amann. 2022. KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 245–250, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text (Agirrezabal & Amann, LTEDI 2022)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2022.ltedi-1.35.pdf