Classifying topics and detecting topic shifts in political manifestos

Zirn, Cäcilia ; Glavaš, Goran ; Nanni, Federico ; Eichorts, Jason ; Stuckenschmidt, Heiner

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URN: urn:nbn:de:bsz:180-madoc-415521
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: 88-93
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 > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
School of Business Informatics and Mathematics > Semantic Web (Juniorprofessur) (Ponzetto 2013-2015)
Subject: 004 Computer science, internet
Abstract: General political topics, like social security and foreign affairs, recur in electoral manifestos across countries. The Comparative Manifesto Project collects and manually codes manifestos of political parties from all around the world, detecting political topics at sentence level. Since manual coding is time-consuming and allows for annotation inconsistencies, in this work we present an automated approach to topical coding of political manifestos. We first train three independent sentence-level classifiers – one for detecting the topic and two for detecting topic shifts – and then globally optimize their predictions using a Markov Logic network. Experimental results show that the proposed global model achieves high classification performance and significantly outperforms the local sentence-level topic classifier.
Additional information: Online-Ressource

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