Learning Word Ratings for Empathy and Distress from Document-Level User Responses

João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, Lyle Ungar

Abstract
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.
Anthology ID:
2020.lrec-1.206
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1664–1673
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.206
DOI:
Bibkey:
Cite (ACL):
João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, and Lyle Ungar. 2020. Learning Word Ratings for Empathy and Distress from Document-Level User Responses. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1664–1673, Marseille, France. European Language Resources Association.
Cite (Informal):
Learning Word Ratings for Empathy and Distress from Document-Level User Responses (Sedoc et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/2020.lrec-1.206.pdf
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