Abstract
In this paper, we have addressed Task 3 on social anxiety disorder identification and Task 5 on mental illness recognition organized by the SMM4H 2024 workshop. In Task 3, a multi-classification problem has been presented to classify Reddit posts about outdoor spaces into four categories: Positive, Neutral, Negative, or Unrelated. Using the pre-trained RoBERTa-base model along with techniques like Mean pooling, CLS, and Attention Head, we have scored an F1-Score of 0.596 on the test dataset for Task 3. Task 5 aims to classify tweets into two categories: those describing a child with conditions like ADHD, ASD, delayed speech, or asthma (class 1), and those merely mentioning a disorder (class 0). Using the pre-trained RoBERTa-large model, incorporating a weighted ensemble of the last 4 hidden layers through concatenation and mean pooling, we achieved an F1 Score of 0.928 on the test data for Task 5.- Anthology ID:
- 2024.smm4h-1.7
- Volume:
- Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dongfang Xu, Graciela Gonzalez-Hernandez
- Venues:
- SMM4H | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 28–31
- Language:
- URL:
- https://aclanthology.org/2024.smm4h-1.7
- DOI:
- Cite (ACL):
- Md Ayon Mia, Mahshar Yahan, Hasan Murad, and Muhammad Khan. 2024. Golden_Duck at #SMM4H 2024: A Transformer-based Approach to Social Media Text Classification. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 28–31, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Golden_Duck at #SMM4H 2024: A Transformer-based Approach to Social Media Text Classification (Mia et al., SMM4H-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.smm4h-1.7.pdf