Abstract
This submission presents our approach for depression detection in social media text. The methodology includes data collection, preprocessing - SMOTE, feature extraction/selection - TF-IDF and Glove, model development- SVM, CNN and Bi-LSTM, training, evaluation, optimisation, and validation. The proposed methodology aims to contribute to the accurate detection of depression.- Anthology ID:
- 2023.ltedi-1.40
- Volume:
- Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Bharathi R. Chakravarthi, B. Bharathi, Joephine Griffith, Kalika Bali, Paul Buitelaar
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 262–265
- Language:
- URL:
- https://aclanthology.org/2023.ltedi-1.40
- DOI:
- Cite (ACL):
- Koushik L, Hariharan R. L, and Anand Kumar M. 2023. Interns@LT-EDI : Detecting Signs of Depression from Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 262–265, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Interns@LT-EDI : Detecting Signs of Depression from Social Media Text (L et al., LTEDI-WS 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.ltedi-1.40.pdf