Abstract
Detecting fine-grained emotions in online health communities provides insightful information about patients’ emotional states. However, current computational approaches to emotion detection from health-related posts focus only on identifying messages that contain emotions, with no emphasis on the emotion type, using a set of handcrafted features. In this paper, we take a step further and propose to detect fine-grained emotion types from health-related posts and show how high-level and abstract features derived from deep neural networks combined with lexicon-based features can be employed to detect emotions.- Anthology ID:
- D18-1147
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1160–1166
- Language:
- URL:
- https://aclanthology.org/D18-1147
- DOI:
- 10.18653/v1/D18-1147
- Cite (ACL):
- Hamed Khanpour and Cornelia Caragea. 2018. Fine-Grained Emotion Detection in Health-Related Online Posts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1160–1166, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Fine-Grained Emotion Detection in Health-Related Online Posts (Khanpour & Caragea, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D18-1147.pdf