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.

通过比较使用机器学习模型与多个大型语言模型在波兰政治文本情感强度预测方面的表现，研究发现虽然受过监督的模型通常表现优于大型语言模型，但由于数据标注的高昂成本，大型语言模型在资源匮乏的语言环境中是可行的替代选择，该研究强调了大型语言模型在低资源语言环境中的潜力，并强调了对不同语言和连续特征上情感强度预测及其应用的进一步研究的需求，该研究的意义在于基于资源可用性和任务的具体要求为研究人员和从业者选择正确的情感预测方法提供细致的决策过程。

预测波兰政治文本中的情绪强度：监督模型与资源匮乏语言中的大型语言模型比较

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

Labeling corpora constitutes a bottleneck to create models for new tasks or
domains. Large language models mitigate the issue with automatic corpus
labeling methods, particularly for categorical annotations. Some NLP tasks such
as emotion intensity prediction, however, require text regression, but there is
no work on automating annotations for continuous label assignments. Regression
is considered more challenging than classification: The fact that humans
perform worse when tasked to choose values from a rating scale lead to
comparative annotation methods, including best-worst scaling. This raises the
question if large language model-based annotation methods show similar
patterns, namely that they perform worse on rating scale annotation tasks than
on comparative annotation tasks. To study this, we automate emotion intensity
predictions and compare direct rating scale predictions, pairwise comparisons
and best-worst scaling. We find that the latter shows the highest reliability.
A transformer regressor fine-tuned on these data performs nearly on par with a
model trained on the original manual annotations.

大语言模型对于分类注释的自动化主要用于处理标注语料库的瓶颈问题，但对于连续标签分配的自动化注释尚无相关研究。本研究通过对情感强度进行自动化预测，并比较直接使用评分标注、两两比较和最佳 - 最差比例三种方法，发现最后一种方法具有最高的可靠性。在这些数据上微调的 Transformer 回归器表现几乎与原始手动标注模型相当。