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Natural language processing-based algorithmic decision support system in identifying psychological conflicts using

Jay Kumar Ranjan1, Madhubanti Sinha1, Arundhati Kar1

  • 1Department of Psychology, Banaras Hindu University Varanasi, Uttar Pradesh, India.

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Summary
This summary is machine-generated.

Natural Language Processing (NLP) enhances Thematic Apperception Test (TAT) scoring. An NLP-based algorithmic decision support system (NBADSS) objectively quantifies TAT stories, aiding in uncovering intrapsychic conflicts.

Keywords:
Natural language processingTATpsychological conflicts

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Area of Science:

  • Psychology
  • Computer Science
  • Psychometrics

Background:

  • The Thematic Apperception Test (TAT) is a widely used personality assessment tool in clinical settings.
  • It involves analyzing written stories to understand unconscious dynamics, including needs, emotions, and defense mechanisms.
  • Natural Language Processing (NLP) offers text analysis techniques applicable to psychological attributes and behavior prediction.

Purpose of the Study:

  • To evaluate the accuracy and precision of an NLP-based algorithmic decision support system (NBADSS) for scoring TAT protocols.
  • To determine if NBADSS can effectively score across various psychometric dimensions of the TAT.

Main Methods:

  • An NLP-based algorithmic decision support system (NBADSS) was developed using the 'nltk' Python library.
  • 87 healthy participants (aged 17-40) completed TAT assessments.
  • TAT protocols were scored manually and then by NBADSS, with results compared using Cohen's Kappa and ROC analysis.

Main Results:

  • The NBADSS demonstrated high sensitivity and specificity in assessing needs, pressures, emotions, and defense mechanisms within TAT stories.
  • The system proved to be a valuable adjunct for the objective quantification of TAT protocols.

Conclusions:

  • The NBADSS provides an objective method for analyzing TAT stories.
  • It aids in uncovering underlying intrapsychic conflicts projected through the narratives.