Moral sentiment is the attitude of praise or blame that people exhibit toward a concept or event. For instance, the sentence “the former Republican condemned Trump for his treasonous behaviour” expresses a negative moral sentiment. The inquiry of moral sentiment has traditionally been pursued in philosophy, with later extensions to social psychology, and more recent developments in natural language processing and computational social science. Despite this extensive line of research, formal approaches to moral sentiment change—how people’s moral sentiments shift over time—are still in their infancy. We present a computational approach for investigating moral sentiment change via large-scale analysis of longitudinal text corpora. We construct moral environment over time by exploiting implicit moral biases learned from diachronic word embeddings, and we demonstrate how a parameter-free model supports inference of historical shifts in moral sentiment of the public toward concepts such as slavery and racism, both at the dichotomic and fine-grained levels. Our work provides opportunities for preemptive detection of moral sentiment change in society.
Work by Jing Yi Xie, Renato Ferreira Pinto Junior, Graeme Hirst, and Yang Xu.