In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection. However, the specific emotion of guilt has received limited attention in this field. In this research, we explore the applicability of three transformer-based language models for detecting guilt in text and compare their performance for general emotion detection and guilt detection. Our proposed model outformed BERT and RoBERTa models by two and one points respectively. Additionally, we analyze the challenges in developing accurate guilt-detection models and evaluate our model's effectiveness in detecting related emotions like "shame" through qualitative analysis of results.

翻译：近年来，语言模型与深度学习技术彻底变革了自然语言处理任务，包括情感检测。然而，在情感领域中，特定情感"内疚"的研究却相对匮乏。本研究探索了三种基于Transformer的语言模型在文本中检测内疚感的适用性，并比较了它们在通用情感检测与内疚感检测任务上的性能表现。我们提出的模型在性能上分别超出BERT和RoBERTa模型两个百分点和一个百分点。此外，我们还分析了开发精准内疚感检测模型所面临的挑战，并通过结果定性分析评估了模型在检测"羞愧"等相关情感方面的有效性。