Machine Learning and Statistical Approaches to Measuring Similarity of Political Parties
This addresses the labor-intensive and subjective nature of expert-based assessments in political science, offering a data-driven alternative for measuring party similarity.
The paper tackles the problem of mapping political party systems to metric policy spaces by applying natural language processing and text similarity measures to party political programs, evaluating them against expert surveys and other measures to potentially replace subjective expert judgments.
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.