Predicting Emotion Intensity in Polish Political Texts: Comparing Supervised Models and Large Language Models in a Resource-Poor Language

TL;DR

This paper compares supervised models and large language models for predicting emotion intensity in Polish political texts, highlighting the strengths and limitations of each approach in a resource-poor language context.

Contribution

It provides a comparative analysis of LLMs and supervised models for emotion intensity prediction in Polish, a low-resource language, demonstrating the potential of LLMs despite lower accuracy.

Findings

Supervised model outperforms LLMs in accuracy.

LLMs are a viable alternative with high annotation costs.

Study emphasizes the importance of resource considerations in model choice.

Abstract

This study explores the use of large language models (LLMs) to predict emotion intensity in Polish political texts, a resource-poor language context. The research compares the performance of several 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.