# Guilt Detection in Text: A Step Towards Understanding Complex Emotions

arXiv: https://arxiv.org/abs/2303.03510 · HTML: https://mlsanity.com/paper/2303.03510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019)
- [PolyHope: Two-Level Hope Speech Detection from Tweets](https://mlsanity.com/paper/2210.14136/) (2022)
- [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)
- [ReDDIT: Regret Detection and Domain Identification from Text](https://mlsanity.com/paper/2212.07549/) (2022)

## 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)

We introduce a novel Natural Language Processing (NLP) task called Guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area.