Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.
@article{arxiv.2109.04535,
title = {Identifying Morality Frames in Political Tweets using Relational Learning},
author = {Shamik Roy and Maria Leonor Pacheco and Dan Goldwasser},
journal= {arXiv preprint arXiv:2109.04535},
year = {2021}
}