Identifying Morality Frames in Political Tweets using Relational Learning

• URL: http://arxiv.org/abs/2109.04535v1
• Date: Thu, 9 Sep 2021 19:48:57 GMT
• Title: Identifying Morality Frames in Political Tweets using Relational Learning
• Authors: Shamik Roy, Maria Leonor Pacheco, Dan Goldwasser
• Abstract summary: Moral sentiment is motivated by its targets, which can correspond to individuals or collective entities. We introduce morality frames, a representation framework for organizing moral attitudes directed at different entities. We propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly.
• Score: 27.047907641503762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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