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
Document Type:	Conference or workshop publication
Year of publication:	2012
Book title:	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
The title of a journal, publication series:	Lecture Notes in Computer Science
Volume:	7337
Page range:	11-22
Date of the conference:	June 26 - 28, 2012
Publisher:	Bouma, Gosse
Place of publication:	Berlin [u.a.]
Publishing house:	Springer
ISBN:	978-3-642-31177-2 , 978-3-642-31178-9
ISSN:	0302-9743 , 1611-3349
Publication language:	English
Institution:	Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES) School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject:	004 Computer science, internet
Keywords (English):	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.