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This study evaluates two different natural language processing techniques: the normalised co-occurrence (PMI) versus neural networks. In contrast to most previous studies, the focus is on the context of a proportional representation system-Sweden-, where the parties in parliament tend to form coalitions. We test the models by collecting data from t...
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Context 1
... this finding is likely due to the pre-training data set dominating the embeddings. In the model that was further fine-tuned for additional epochs, we see that the differences between the parties' focus appear clearer, in particular for the 2010-2020 period (Figure 4). The military and in-ternational relations dimensions becomes slightly less pronounced for the Social Democrats, who emphasise the "governance" dimension, while the Moderates' focus on "sovereignty". ...
Citations
... For example, Papasavva et al. [22] apply Word2Vec to find words associated with QAnon. It has also been used to compare semantic contexts: e.g. the language use of parliamentary motions of the opposing Swedish political parties [23]. While our goal is somewhat different, the approach is similar. ...
By identifying and characterising the narratives told in news media we can better understand political and societal processes. The problem is challenging from the perspective of natural language processing because it requires a combination of quantitative and qualitative methods. This paper reports on work in progress, which aims to build a human-in-the-loop pipeline for analysing how the variation of narrative themes across different domains, based on topic modelling and word embeddings. As an illustration, we study the language associated with the threat narrative in British news media.
