ReDDIT: Regret Detection and Domain Identification from Text
This work addresses the challenge of understanding emotional language in online text for natural language processing and social media design, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of analyzing regret expression on social media by creating a dataset of Reddit texts classified into regret types and identifying common domains, finding that users most often express regret for past actions in relationships and that deep learning models with GloVe embeddings outperformed others.
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.