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Machine learning-based guilt detection in text.

Abdul Gafar Manuel Meque1,2, Nisar Hussain1, Grigori Sidorov3

  • 1Instituto Politécnico Nacional (IPN), Centro de Investigación en Computación (CIC), Mexico City, Mexico.

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This summary is machine-generated.

Researchers developed guilt detection, a new Natural Language Processing (NLP) task. Using the VIC dataset and machine learning, they achieved 72% f1 score in identifying guilt in text.

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Area of Science:

  • Computational Linguistics
  • Affective Computing
  • Natural Language Processing (NLP)

Background:

  • Guilt is a complex emotion crucial for social interaction.
  • Previous NLP research has not specifically addressed guilt detection.
  • A need exists for fine-grained emotional analysis in text.

Purpose of the Study:

  • To introduce and define the novel NLP task of guilt detection.
  • To create a labeled dataset for training and evaluating guilt detection models.
  • To establish baseline performance for guilt detection using traditional machine learning.

Main Methods:

  • Development of the VIC dataset, comprising 4622 binarized texts (guilt/no-guilt).
  • Utilized traditional machine learning algorithms.
  • Employed feature extraction techniques including bag-of-words and TF-IDF.

Main Results:

  • The highest-performing model achieved a 72% F1 score on the guilt detection task.
  • Demonstrated the feasibility of detecting guilt using NLP methods.
  • Established a benchmark for future guilt detection research.

Conclusions:

  • This study presents the first NLP approach to guilt detection.
  • The VIC dataset provides a valuable resource for advancing research in this area.
  • Future work can explore more sophisticated NLP models for improved guilt detection accuracy.