  Scaling analysis is a technique in computational political science that
assigns a political actor (e.g. politician or party) a score on a predefined
scale based on a (typically long) body of text (e.g. a parliamentary speech or
an election manifesto). For example, political scientists have often used the
left--right scale to systematically analyse political landscapes of different
countries. NLP methods for automatic scaling analysis can find broad
application provided they (i) are able to deal with long texts and (ii) work
robustly across domains and languages. In this work, we implement and compare
two approaches to automatic scaling analysis of political-party manifestos:
label aggregation, a pipeline strategy relying on annotations of individual
statements from the manifestos, and long-input-Transformer-based models, which
compute scaling values directly from raw text. We carry out the analysis of the
Comparative Manifestos Project dataset across 41 countries and 27 languages and
find that the task can be efficiently solved by state-of-the-art models, with
label aggregation producing the best results.
