Application of Natural Language Processing to the electoral manifestos of parties characterised by populist rhetoric in Central and Eastern Europe

Abstract

The aim of this paper is to find patterns regarding the rhetoric employed by actors characterised by populist rhetoric in Central and Eastern Europe (CEE). To do so we examine all available electoral manifestos of all parties in CEE during the 2000–2022 period and we use Natural Language Processing with the goal of extracting meaning from a large number of texts. The analyses focus on most frequent topics using Topic Modelling, whether the manifestos express positive or negative emotions using Sentiment Analysis, and on the analysis of word frequencies. The analysis reveals a complex landscape characterized by both common themes and country-specific variations for what concerns populist rhetoric remove in the region. Populist parties consistently employ more negative sentiments in their manifestos compared to non-populist parties, regardless of being right-wing or left-wing. While they share overarching themes such as anti-EU sentiment, critiques of economic liberalism, religious and ethno-centric language, and anti-corruption narratives, populist parties skilfully tailor their rhetoric to specific national contexts and historical narratives. This adaptability allows them to resonate more effectively with local electorates. The use of negative rhetoric is a tactical choice across the populist spectrum rather than an indicator of ideological radicalism. Notably, populist parties often frame the EU as an obstacle to national sovereignty and economic prosperity while simultaneously acknowledging its benefits. The effectiveness of populist rhetoric appears to be influenced by each country’s economic and institutional context, with countries investing in productivity- and innovation-led growth providing less fertile ground for divisive populist messages.