  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.
