Predicting emotion intensity in Polish political texts: comparing supervised models and large language models in a low-resource language

Hubert Plisiecki (), Piotr Koc, Maria Flakus and Artur Pokropek
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Hubert Plisiecki: Polish Academy of Sciences
Piotr Koc: Polish Academy of Sciences
Maria Flakus: Polish Academy of Sciences
Artur Pokropek: Polish Academy of Sciences

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 4, No 18, 3405-3427

Abstract: Abstract This study explores the use of large language models (LLMs) to predict emotion intensity in Polish political texts, a low-resource language context. The research compares the performance of state-of-the-art LLMs against a supervised model trained on an annotated corpus of 10,000 social media texts, evaluated for the intensity of emotions by expert judges. The findings indicate that while the supervised model generally outperforms LLMs, offering higher accuracy and lower variance, LLMs present a viable alternative, especially given the high costs associated with data annotation. The study highlights the potential of LLMs in low-resource language settings and underscores the need for further research on emotion intensity prediction and its application across different languages and continuous features. The implications suggest a nuanced decision-making process to choose the right approach to emotion prediction for researchers and practitioners based on resource availability and the specific requirements of their tasks.

Keywords: Emotion intensity prediction; Large language models (LLMs); Low-resource languages; Polish political texts; Supervised models; Emotion annotation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02116-8

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