Exploring morality in argumentation

Kobbe, Jonathan ; Rehbein, Ines ; Hulpus, Ioana ; Stuckenschmidt, Heiner

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URL: https://madoc.bib.uni-mannheim.de/57762
Additional URL: https://www.aclweb.org/anthology/2020.argmining-1....
URN: urn:nbn:de:bsz:180-madoc-577626
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: Proceedings of the 7th Workshop on Argument Mining : Barcelona, Spain (Online), December 13, 2020
Page range: 30-40
Conference title: 7th Workshop on Argument Mining
Location of the conference venue: Online
Date of the conference: 13.12.2020
Publisher: Cabrio, Elena ; Villata, Serena
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics, ACL
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.

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