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.
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Stuckenschmidt, H., Zirn, C. (2012). Multi-dimensional Analysis of Political Documents. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_2
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DOI: https://doi.org/10.1007/978-3-642-31178-9_2
Publisher Name: Springer, Berlin, Heidelberg
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