The German Research Council (DFG) will fund a project on Structured Multi-Domain Emotion Analysis from Text (SEAT), proposed by Roman Klinger.
Emotion analysis in natural language processings aims at associating text with emotions, for instance with anger, fear, joy, surprise, disgust or sadness. This task extends sentiment analysis, which adds further qualitative value in applications, for instance in social media analysis, in the analysis of fictional stories or news articles.
Existing research has so far mainly focused on the association of text with specific emotion models from psychological research. The development of methods for detecting phrases in text which denote the emotion experiencer (the character or person who feels the emotion), the emotion theme (the cause of the development of an emotion) as well as the modifiers of an emotion (intensifiers and diminishers) has been neglected.
The new project will aim at filling this gap for multiple domains, like news, novels, or social media.
The job opening for this project can be found here.