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Ansia Dibuja, D., Folgado, M. G., & Sanz, V. (2026). Analyzing polarization among Spanish political elites using machine learning techniques. J. Comput. Soc. Sci., 9(1), 4–26pp.
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.
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