Multi-National Topics Maps for Parliamentary Debate Analysis

Date
2022-01-04
Authors
Schaal, Markus
Davis, Enno
Mueller, Roland M.
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Abstract
In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact.
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Data Analytics, Data Mining and Machine Learning for Social Media, cross-country, latent dirichlet allocation, multi-lingual, parliamentary speech, probabilistic topic modelling
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