  The quantitative analysis of political ideological positions is a difficult
task. In the past, various literature focused on parliamentary voting data of
politicians, party manifestos and parliamentary speech to estimate political
disagreement and polarization in various political systems. However previous
methods of quantitative political analysis suffered from a common challenge
which was the amount of data available for analysis. Also previous methods
frequently focused on a more general analysis of politics such as overall
polarization of the parliament or party-wide political ideological positions.
In this paper, we present a method to analyze ideological positions of
individual parliamentary representatives by leveraging the latent knowledge of
LLMs. The method allows us to evaluate the stance of politicians on an axis of
our choice allowing us to flexibly measure the stance of politicians in regards
to a topic/controversy of our choice. We achieve this by using a fine-tuned
BERT classifier to extract the opinion-based sentences from the speeches of
representatives and projecting the average BERT embeddings for each
representative on a pair of reference seeds. These reference seeds are either
manually chosen representatives known to have opposing views on a particular
topic or they are generated sentences which where created using the GPT-4 model
of OpenAI. We created the sentences by prompting the GPT-4 model to generate a
speech that would come from a politician defending a particular position.
