Multi-dimensional analysis of political documents

Authors:
Heiner Stuckenschmidt;Cäcilia Zirn
Affiliations:
KR & KM Research Group, University of Mannheim, Germany;KR & KM Research Group, University of Mannheim, Germany
Venue:
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Year:
2012

Citing 4
Cited 0

Latent dirichlet allocation

The Journal of Machine Learning Research
TextTiling: segmenting text into multi-paragraph subtopic passages

Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network

NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A framework for incorporating general domain knowledge into latent Dirichlet allocation using first-order logic

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two

Quantified Score

Hi-index	0.00

Visualization

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.