@inproceedings{huguet-cabot-etal-2020-pragmatics,
title = "{T}he {P}ragmatics behind {P}olitics: {M}odelling {M}etaphor, {F}raming and {E}motion in {P}olitical {D}iscourse",
author = "Huguet Cabot, Pere-Llu{\'\i}s and
Dankers, Verna and
Abadi, David and
Fischer, Agneta and
Shutova, Ekaterina",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.402",
doi = "10.18653/v1/2020.findings-emnlp.402",
pages = "4479--4488",
abstract = "There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others. Metaphor-rich and emotion-eliciting communication strategies are ubiquitous in political rhetoric, according to social science research. Yet, none of the existing computational models of political discourse has incorporated these phenomena. In this paper, we present the first joint models of metaphor, emotion and political rhetoric, and demonstrate that they advance performance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues.",
}
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<abstract>There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others. Metaphor-rich and emotion-eliciting communication strategies are ubiquitous in political rhetoric, according to social science research. Yet, none of the existing computational models of political discourse has incorporated these phenomena. In this paper, we present the first joint models of metaphor, emotion and political rhetoric, and demonstrate that they advance performance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues.</abstract>
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%0 Conference Proceedings
%T The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse
%A Huguet Cabot, Pere-Lluís
%A Dankers, Verna
%A Abadi, David
%A Fischer, Agneta
%A Shutova, Ekaterina
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F huguet-cabot-etal-2020-pragmatics
%X There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others. Metaphor-rich and emotion-eliciting communication strategies are ubiquitous in political rhetoric, according to social science research. Yet, none of the existing computational models of political discourse has incorporated these phenomena. In this paper, we present the first joint models of metaphor, emotion and political rhetoric, and demonstrate that they advance performance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues.
%R 10.18653/v1/2020.findings-emnlp.402
%U https://aclanthology.org/2020.findings-emnlp.402
%U https://doi.org/10.18653/v1/2020.findings-emnlp.402
%P 4479-4488
Markdown (Informal)
[The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse](https://aclanthology.org/2020.findings-emnlp.402) (Huguet Cabot et al., Findings 2020)
ACL