Annika Fredén

This study evaluates two different natural language processing tech-
niques: the normalised co-occurrence (PMI) versus neural networks. In
contrast to most previous studies, the focus is on the context of a propor-
tional representation system – Sweden –, where the parties in parliament
tend to form coalitions. We test the models by collecting data from the
national parliament (Swedish Riksdag) and using the Swedish language for
training sets and dictionaries. The tests focus primarily on the meaning
and attention that the party representatives confer to important terms,
as well as left-right ideological positioning, with an emphasis on the di-
mension of “security”. The analysis covers parliamentary motions from
the two main competitor parties (Moderates and Social Democrats) over
two time spans, from 1988-2009 and 2010-2020. The two models delivered
different foci of keywords, and we found that balancing pre-training and
fine-tuning was crucial to obtaining differences between parties in the neu-
ral network approach. The PMI model benefited from a larger context
window, and the neural network model (word2vec) from a smaller one.
We discuss the results in relation to future opportunities to learn about
political vocabularies and the nature of conflict in parliamentary politics.