Abstract
This paper describes our submission to Social Media Mining for Health (SMM4H) 2022 Shared Task 8, aimed at detecting self-reported chronic stress on Twitter. Our approach leverages a pre-trained transformer model (RoBERTa) in combination with a Bidirectional Long Short-Term Memory (BiLSTM) network trained on a diverse set of psycholinguistic features. We handle the class imbalance issue in the training dataset by augmenting it by another dataset used for stress classification in social media.- Anthology ID:
- 2022.smm4h-1.5
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16–18
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.5
- DOI:
- Cite (ACL):
- Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, and Elma Kerz. 2022. MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 16–18, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress (Zanwar et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.smm4h-1.5.pdf