@inproceedings{khanpour2018fine-grained,
author = {Khanpour, Hamed and Caragea, Cornelia},
title = {Fine-Grained Emotion Detection in Health-Related Online Posts},
organization = {ACL},
booktitle = {EMNLP},
year = {2018},
month = {November},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/fine-grained-emotion-detection-in-health-related-online-posts/},
}