%0 Conference Proceedings %T Identifying Morality Frames in Political Tweets using Relational Learning %A Roy, Shamik %A Pacheco, Maria Leonor %A Goldwasser, Dan %Y Moens, Marie-Francine %Y Huang, Xuanjing %Y Specia, Lucia %Y Yih, Scott Wen-tau %S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing %D 2021 %8 November %I Association for Computational Linguistics %C Online and Punta Cana, Dominican Republic %F roy-etal-2021-identifying %X 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. %R 10.18653/v1/2021.emnlp-main.783 %U https://aclanthology.org/2021.emnlp-main.783 %U https://doi.org/10.18653/v1/2021.emnlp-main.783 %P 9939-9958