Multi-dimensional Analysis of Political Documents

DOI:	https://doi.org/10.1007/978-3-642-31178-9_2
URL:	http://dl.acm.org/citation.cfm?id=2368176
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2012
Buchtitel:	Natural Language Processing and Information Systems : 17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012, Groningen, The Netherlands, June 26-28, 2012. Proceedings
Titel einer Zeitschrift oder einer Reihe:	Lecture Notes in Computer Science
Band/Volume:	7337
Seitenbereich:	11-22
Veranstaltungsdatum:	June 26 - 28, 2012
Herausgeber:	Bouma, Gosse
Ort der Veröffentlichung:	Berlin [u.a.]
Verlag:	Springer
ISBN:	978-3-642-31177-2 , 978-3-642-31178-9
ISSN:	0302-9743 , 1611-3349
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES) Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Fachgebiet:	004 Informatik
Freie Schlagwörter (Englisch):	Topic Models, Political Science
Abstract:	Abstract. Automatic content analysis is more and more becoming an accepted research method in social science. In political science researchers are using party manifestos and transcripts of political speeches to analyze the positions of diﬀerent actors. Existing approaches are limited to a single dimension, in particular, they cannot distinguish between the positions with respect to a speciﬁc topic. In this paper, we propose a method for analyzing and comparing documents according to a set of predeﬁned topics that is based on an extension of Latent Dirichlet Allocation for inducing knowledge about relevant topics. We validate the method by showing that it can reliably guess which member of a coalition was assigned a certain ministry based on a comparison of the parties’ election manifestos with the coalition contract.