@inproceedings{roy-etal-2021-identifying,
title = "Identifying Morality Frames in Political Tweets using Relational Learning",
author = "Roy, Shamik and
Pacheco, Maria Leonor and
Goldwasser, Dan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.783/",
doi = "10.18653/v1/2021.emnlp-main.783",
pages = "9939--9958",
abstract = "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."
}
Markdown (Informal)
[Identifying Morality Frames in Political Tweets using Relational Learning](https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.783/) (Roy et al., EMNLP 2021)
ACL