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        <title>Identifying Morality Frames in Political Tweets using Relational Learning</title>
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        <namePart type="given">Shamik</namePart>
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        <namePart type="given">Leonor</namePart>
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    <name type="personal">
        <namePart type="given">Dan</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.783</identifier>
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%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

Markdown (Informal)

[Identifying Morality Frames in Political Tweets using Relational Learning](https://aclanthology.org/2021.emnlp-main.783) (Roy et al., EMNLP 2021)

• Identifying Morality Frames in Political Tweets using Relational Learning (Roy et al., EMNLP 2021)

ACL

• Shamik Roy, Maria Leonor Pacheco, and Dan Goldwasser. 2021. Identifying Morality Frames in Political Tweets using Relational Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9939–9958, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.