  Despite the excellent performance of black box approaches to modeling
sentiment and emotion, lexica (sets of informative words and associated
weights) that characterize different emotions are indispensable to the NLP
community because they allow for interpretable and robust predictions. Emotion
analysis of text is increasing in popularity in NLP; however, manually creating
lexica for psychological constructs such as empathy has proven difficult. This
paper automatically creates empathy word ratings from document-level ratings.
The underlying problem of learning word ratings from higher-level supervision
has to date only been addressed in an ad hoc fashion and has not used deep
learning methods. We systematically compare a number of approaches to learning
word ratings from higher-level supervision against a Mixed-Level Feed Forward
Network (MLFFN), which we find performs best, and use the MLFFN to create the
first-ever empathy lexicon. We then use Signed Spectral Clustering to gain
insights into the resulting words. The empathy and distress lexica are publicly
available at: http://www.wwbp.org/lexica.html.
