Topic-based agreement and disagreement in US electoral manifestos


Menini, Stefano ; Nanni, Federico ; Ponzetto, Simone Paolo ; Tonelli, Sara


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URL: https://ub-madoc.bib.uni-mannheim.de/42490
Additional URL: http://emnlp2017.net/
URN: urn:nbn:de:bsz:180-madoc-424901
Document Type: Conference or workshop publication
Year of publication: 2017
Book title: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Page range: 2928-2934
Conference title: EMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Location of the conference venue: Kopenhagen
Date of the conference: 07.-11.09.2017
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-945626-83-8
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik III (Ponzetto 2016-)
Subject: 004 Computer science, internet
Abstract: We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.

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Menini, Stefano ; Nanni, Federico ORCID: 0000-0003-2484-4331 ; Ponzetto, Simone Paolo ; Tonelli, Sara Topic-based agreement and disagreement in US electoral manifestos. Open Access 2928-2934 In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017) Stroudsburg, PA EMNLP 2017: Conference on Empirical Methods in Natural Language Processing (Kopenhagen) [Conference or workshop publication]
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