Leveraging the power of transformers for guilt detection in text
Abdul Gafar Manuel Meque Jason Angel Grigori Sidorov Alexander Gelbukh
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.