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Machine learning-based guilt detection in text.
Meque, Abdul Gafar Manuel; Hussain, Nisar; Sidorov, Grigori; Gelbukh, Alexander.
Afiliação
  • Meque AGM; Instituto Politécnico Nacional (IPN), Centro de Investigación en Computación (CIC), Mexico City, Mexico.
  • Hussain N; Faculdade de Economia e Gestao, Catholic University of Mozambique, Beira, 2100, Mozambique.
  • Sidorov G; Instituto Politécnico Nacional (IPN), Centro de Investigación en Computación (CIC), Mexico City, Mexico.
  • Gelbukh A; Instituto Politécnico Nacional (IPN), Centro de Investigación en Computación (CIC), Mexico City, Mexico. sidorov@cic.ipn.mx.
Sci Rep ; 13(1): 11441, 2023 07 15.
Article em En | MEDLINE | ID: mdl-37454207
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Linguagem Natural / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: México País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Linguagem Natural / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: México País de publicação: Reino Unido