Autores
Balouchzahi Fazlourrahman
Butt Sabur
Sidorov Grigori
Gelbukh Alexander
Título ReDDIT: Regret detection and domain identification from text
Tipo Revista
Sub-tipo JCR
Descripción Expert Systems with Applications
Resumen Regret is a universal emotion that arises from sadness, disappointment, or remorse about something that has occurred or that one has done or failed to do in the past. It typically involves wishing that a different decision had been made or that a different action had been taken. Although regret has been studied in various contexts such as psychology, neuroscience, and philosophy, its expression and analysis on social media using natural language processing techniques is a relatively recent research topic that warrants further investigation. 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. © 2023 Elsevier Ltd
Observaciones DOI 10.1016/j.eswa.2023.120099
Lugar Oxford
País Reino Unido
No. de páginas Article number 3983
Vol. / Cap. v. 225
Inicio 2023-09-01
Fin
ISBN/ISSN