@misc{e3e5aaf3-f5fc-416e-a8cf-4c9fab4c9502,
  abstract     = {{This study evaluates two different natural language processing tech-<br/>niques: the normalised co-occurrence (PMI) versus neural networks. In<br/>contrast to most previous studies, the focus is on the context of a propor-<br/>tional representation system – Sweden –, where the parties in parliament<br/>tend to form coalitions. We test the models by collecting data from the<br/>national parliament (Swedish Riksdag) and using the Swedish language for<br/>training sets and dictionaries. The tests focus primarily on the meaning<br/>and attention that the party representatives confer to important terms,<br/>as well as left-right ideological positioning, with an emphasis on the di-<br/>mension of “security”. The analysis covers parliamentary motions from<br/>the two main competitor parties (Moderates and Social Democrats) over<br/>two time spans, from 1988-2009 and 2010-2020. The two models delivered<br/>different foci of keywords, and we found that balancing pre-training and<br/>fine-tuning was crucial to obtaining differences between parties in the neu-<br/>ral network approach. The PMI model benefited from a larger context<br/>window, and the neural network model (word2vec) from a smaller one.<br/>We discuss the results in relation to future opportunities to learn about<br/>political vocabularies and the nature of conflict in parliamentary politics.}},
  author       = {{Fredén, Annika and Johansson, Moa and Kisić-Merino, Pasko and Saynova, Denitsa}},
  language     = {{eng}},
  title        = {{A Comparison of Language Processing Models in Political Analysis : Evidence from Sweden}},
  url          = {{https://www.researchgate.net/profile/Annika-Freden/publication/354997332_A_Comparison_of_Language_Processing_Models_in_Political_Analysis_Evidence_from_Sweden/links/615727fa61a8f46670997c9f/A-Comparison-of-Language-Processing-Models-in-Political-Analysis-Evidence-from-Sweden.pdf}},
  year         = {{2021}},
}