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Predicting Personality and Psychological Distress Using Natural Language Processing: A Study Protocol.

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  • 1School of Psychology, Korea University, Seoul, South Korea.

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Summary
This summary is machine-generated.

This study develops interview questions for the Five-Factor Model (FFM) of personality. Natural language processing (NLP) analyzes interview data to predict personality and psychological distress, bridging psychology and computer science.

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machine learningnatural language processingpersonality predictionpsychological distress predictionthe five-factor model of personality

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Area of Science:

  • Psychology
  • Computer Science
  • Natural Language Processing

Background:

  • Self-report questionnaires are common but have limitations.
  • Machine learning and NLP are increasingly used in psychology.
  • Bridging computer science and psychology research is needed due to data and modeling challenges.

Purpose of the Study:

  • Develop semi-structured interview and open-ended questions for Five-Factor Model (FFM) personality assessments.
  • Collect personality-related text data using interviews and self-report measures.
  • Examine the relationship between natural language data, FFM constructs, and psychological distress to validate NLP-based personality prediction.

Main Methods:

  • Phase I: Developed and refined interview questions with psychology experts.
  • Phase II: Recruit 300 Korean adults via online survey for interviews and self-report measures.
  • Analyze text data using NLP and integrate with survey results to predict FFM personality and psychological distress.

Main Results:

  • Pilot study (Phase I) acquired personality text data from 59 Korean adults.
  • Interview questions finalized based on expert feedback.
  • Phase II data collection and analysis are ongoing to validate NLP models.

Conclusions:

  • The study aims to establish a validated method for NLP-based personality assessment.
  • Findings will contribute to bridging the gap between computational and psychological research.
  • This approach may enhance the prediction of personality and psychological distress.