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        <title>Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory</title>
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        <namePart type="given">Shamik</namePart>
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            <title>Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media</title>
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            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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    <abstract>The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the Moral Foundation Theory in tweets by US politicians on two politically divisive issues - Gun Control and Immigration. We define the nuanced stance of politicians on these two topics by the grades given by related organizations to the politicians. First, we identify moral foundations in tweets from a huge corpus using deep relational learning. Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic. We also found substantial differences in moral foundation usage by different political parties when they address different entities. All of these results indicate the need for more intense research in this area.</abstract>
    <identifier type="citekey">roy-goldwasser-2021-analysis</identifier>
    <identifier type="doi">10.18653/v1/2021.socialnlp-1.1</identifier>
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%0 Conference Proceedings
%T Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory
%A Roy, Shamik
%A Goldwasser, Dan
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F roy-goldwasser-2021-analysis
%X The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the Moral Foundation Theory in tweets by US politicians on two politically divisive issues - Gun Control and Immigration. We define the nuanced stance of politicians on these two topics by the grades given by related organizations to the politicians. First, we identify moral foundations in tweets from a huge corpus using deep relational learning. Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic. We also found substantial differences in moral foundation usage by different political parties when they address different entities. All of these results indicate the need for more intense research in this area.
%R 10.18653/v1/2021.socialnlp-1.1
%U https://aclanthology.org/2021.socialnlp-1.1
%U https://doi.org/10.18653/v1/2021.socialnlp-1.1
%P 1-13

Markdown (Informal)

[Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory](https://aclanthology.org/2021.socialnlp-1.1) (Roy & Goldwasser, SocialNLP 2021)

• Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory (Roy & Goldwasser, SocialNLP 2021)

ACL

• Shamik Roy and Dan Goldwasser. 2021. Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory. In Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, pages 1–13, Online. Association for Computational Linguistics.