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

1 citations · 1 across the 1 of their papers we have counts for

collaborators

1 paper

cs.CL2024★ 1 cited

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

Hubert Plisiecki, Piotr Koc, Maria Flakus +1

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 perf…