TopFish: topic-based analysis of political position in US electoral campaigns

Nanni, Federico ; Zirn, Cäcilia ; Glavaš, Goran ; Eichorst, Jason ; Ponzetto, Simone Paolo

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URN: urn:nbn:de:bsz:180-madoc-415501
Document Type: Conference or workshop publication
Year of publication: 2016
Book title: PolText 2016 : The International Conference on the Advances in Computational Analysis of Political Text : proceedings of the conference : sponsored by the European Social Fund, Operational Programme Efficient Human Resources 2014–2020
Page range: 61-67
Conference title: International Conference on the Advances in Computational Analysis of Political Text
Location of the conference venue: Dubrovnik, Croatia
Date of the conference: 14-16 July 2016
Publisher: Širinić, Daniela
Place of publication: Zagreb
Publishing house: University of Zagreb
ISBN: 978-953-6457-92-2 , 978-953-184-220-4
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): text scaling , text classification , topic analysis , natural language processing
Abstract: In this paper we present TopFish, a multilevel computational method that integrates topic detection and political scaling and shows its applicability for a temporal aspect analysis of political campaigns (preprimary elections, primary elections, and general elections). It enables researchers to perform a range of multidimensional empirical analyses, ultimately allowing them to better understand how candidates position themselves during elections, with respect to a specific topic. The approach has been employed and tested on speeches from the 2008, 2012, and the (ongoing) 2016 US presidential campaigns.
Additional information: Online-Ressource

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