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Multi-National Topics Maps for Parliamentary Debate Analysis
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.
Keyword: cross-country; Data Analytics; Data Mining and Machine Learning for Social Media; latent dirichlet allocation; multi-lingual; parliamentary speech; probabilistic topic modelling
URL: http://hdl.handle.net/10125/79700
https://doi.org/10.24251/HICSS.2022.366
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