Fine-Grained Emotion Detection in Health-Related Online Posts

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.