Fine-Grained Emotion Detection in Health-Related Online Posts

Hamed Khanpour, Cornelia Caragea


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
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D18-1147.pdf