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<p><?tk 4?>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.</p>

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Analyzing polarization among Spanish political elites using machine learning techniques

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Daniel Ansia Dibuja,
  • Miguel G. Folgado,
  • Veronica Sanz

摘要

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.