# Leveraging the power of transformers for guilt detection in text

arXiv: https://arxiv.org/abs/2401.07414 · HTML: https://mlsanity.com/paper/2401.07414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Uncovering the Limits of Text-based Emotion Detection](https://mlsanity.com/paper/2109.01900/) (2021)
- [Sharing emotions at scale: The Vent dataset](https://mlsanity.com/paper/1901.04856/) (2019)

## Built on by (most cited first, 1 of 1)

- [GuReT: Distinguishing Guilt and Regret related Text](https://mlsanity.com/paper/2401.16541/) (2024)

## Abstract (arXiv)

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.