2 citations · 3 across the 2 of their papers we have counts for
2 papers
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…
cs.CL2024★ 2 cited
High Risk of Political Bias in Black Box Emotion Inference Models
Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus +1
This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA) in social science research. Machine learning models often reflec…