# ReDDIT: Regret Detection and Domain Identification from Text

arXiv: https://arxiv.org/abs/2212.07549 · HTML: https://mlsanity.com/paper/2212.07549/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets](https://mlsanity.com/paper/1709.04219/) (2017)
- [Cross-lingual Emotion Detection](https://mlsanity.com/paper/2106.06017/) (2021)

## Built on by (most cited first, 3 of 3)

- [Guilt Detection in Text: A Step Towards Understanding Complex Emotions](https://mlsanity.com/paper/2303.03510/) (2023)
- [GuReT: Distinguishing Guilt and Regret related Text](https://mlsanity.com/paper/2401.16541/) (2024)
- [Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models](https://mlsanity.com/paper/2409.02836/) (2024)

## Abstract (arXiv)

In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication.