Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context.
Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression.
The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks.
Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions.
We aim at filling this gap and present a publicly available corpus based on Project Gutenberg, REMAN (Relational EMotion ANnotation), manually annotated for spans which correspond to emotion trigger phrases and entities/events in the roles of experiencers, targets, and causes of the emotion.
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.