2021/09/09 by Shamik Roy, Roy, Shamik, Maria Leonor Pacheco +4 · 5 citations
Computer Science · Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Computers and Society (cs.CY) #Epistemology #FOS: Computer and information sciences #Foundation (evidence) #Hate Speech and Cyberbullying Detection #Ideology #Law #Machine Learning (cs.LG) #Moral disengagement #Morality #Philosophy #Political science #Politics #Psychology #Quality (philosophy) #Representation (politics) #Sentiment Analysis and Opinion Mining #Sentiment analysis #Social cognitive theory of morality #Social psychology #Sociology #Terrorism, Counterterrorism, and Political Violence #cs.AI #cs.CL #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.2109.04535
published in arXiv (Cornell University) (Cornell University) · Accepted to EMNLP 2021
arxiv created 2021/09/09 · openalex publication_date 2021/09/09 · arxiv updated 2021/09/13 · openalex created_date 2021/11/22 · openalex updated_date 2026/08/05
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.