Text- and author-dependent moral foundations classification

ABSTRACT Moral Foundations Theory (MFT) has attracted a great deal of attention in Natural Language Processing (NLP) and social media analysis, including both applications that attempt to infer moral values inference from text, or otherwise use moral foundations information to perform another downstream task. In this work, we address the issue of moral foundations inference from text data according to two perspectives, hereby called text- and author-dependent classification, by presenting a number of experiments to compare traditional text classifiers with more recent approaches based on pre-trained language models in both English and Portuguese languages. Results suggest that moral foundations classification relies heavily on lexical information, and that different models may be more suitable to each task, and leave a number of opportunities for further research in the field.