Identifying Morality Frames in Political Tweets using Relational Learning
Shamik Roy, María Leonor Pacheco, Dan L. Goldwasser · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 highquality 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.