Machine Learning and Statistical Approaches to Measuring Similarity of
  Political Parties
Daria Boratyn Damian Brzyski Beata Kosowska-Gąstoł Jan Rybicki Wojciech Słomczyński Dariusz Stolicki
Mapping political party systems to metric policy spaces is one of the major
methodological problems in political science. At present, in most political
science project this task is performed by domain experts relying on purely
qualitative assessments, with all the attendant problems of subjectivity and
labor intensiveness. We consider how advances in natural language processing,
including large transformer-based language models, can be applied to solve that
issue. We apply a number of texts similarity measures to party political
programs, analyze how they correlate with each other, and -- in the absence of
a satisfactory benchmark -- evaluate them against other measures, including
those based on expert surveys, voting records, electoral patterns, and
candidate networks. Finally, we consider the prospects of relying on those
methods to correct, supplement, and eventually replace expert judgments.