Leveraging the power of transformers for guilt detection in text
This work addresses guilt detection in text for natural language processing applications, but it is incremental as it applies existing transformer methods to a new emotion.
The paper tackled the problem of detecting guilt in text, a specific emotion that has received limited attention, by exploring transformer-based language models and achieved a performance improvement of two points over BERT and one point over RoBERTa.
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.