What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric
E. Liscio (TU Delft - Interactive Intelligence)
Oscar Araque (Universidad Politécnica de Madrid)
Lorenzo Gatti (University of Twente)
I.L. Constantinescu (ETH Zürich)
C.M. Jonker (TU Delft - Interactive Intelligence)
Kyriaki Kalimeri (ISI Foundation)
P.K. Murukannaiah (TU Delft - Interactive Intelligence)
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Abstract
Moral rhetoric influences our judgement. Although social scientists recognize moral expression as domain specific, there are no systematic methods for analyzing whether a text classifier learns the domain-specific expression of moral language or not. We propose Tomea, a method to compare a supervised classifier's representation of moral rhetoric across domains. Tomea enables quantitative and qualitative comparisons of moral rhetoric via an interpretable exploration of similarities and differences across moral concepts and domains. We apply Tomea on moral narratives in thirty-five thousand tweets from seven domains. We extensively evaluate the method via a crowd study, a series of cross-domain moral classification comparisons, and a qualitative analysis of cross-domain moral expression.
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