Analyzing polarization among Spanish political elites using machine learning techniques

Daniel Ansia Dibuja (), Miguel G. Folgado () and Veronica Sanz ()
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Daniel Ansia Dibuja: Technical University of Denmark (DTU)
Miguel G. Folgado: Universidad de Valencia and CSIC
Veronica Sanz: Universidad de Valencia and CSIC

Journal of Computational Social Science, 2026, vol. 9, issue 1, No 4, 26 pages

Abstract: Abstract This study analyzes ideological and affective polarisation in the Spanish Parliament from 2000 to 2022 using Natural Language Processing (NLP) techniques. Parliamentary records were harvested, pre-processed, and analyzed with document embeddings to assess ideological polarisation, and with sentiment analysis models (VADER and Transformer-based) to measure affective polarisation. The findings reveal a significant increase in both ideological and affective divisions, particularly in recent legislative terms. This research contributes new tools for mapping political discourse and provides a rich, publicly available dataset to support further studies on Spanish political elites.

Keywords: Political polarisation; Parliamentary corpus; Elite polarisation; Ideological placement; Affective polarisation; Ideological polarisation; Sentiment analysis; NLP; Document embeddings; Spain; Congreso de los Diputados; Data science (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/s42001-025-00442-3

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