Analyzing Positions and Topics in Political Discussions of the German Bundestag
نویسنده
چکیده
We present ongoing doctoral work on automatically understanding the positions of politicians with respect to those of the party they belong to. To this end, we use textual data, namely transcriptions of political speeches from meetings of the German Bundestag, and party manifestos, in order to automatically acquire the positions of political actors and parties, respectively. We discuss a variety of possible supervised and unsupervised approaches to determine the topics of interest and compare positions, and propose to explore an approach based on topic modeling techniques for these tasks.
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