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A vectorial semantics approach to personality assessment.

Yair Neuman1, Yochai Cohen1

  • 1Department of Education, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

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This study introduces a novel computational approach for personality assessment using vector semantics. The method accurately predicts personality factors and disorders from written text, advancing automated personality analysis.

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Area of Science:

  • Computational Psychology
  • Natural Language Processing
  • Psychometrics

Background:

  • Traditional personality assessment often overlooks computational models.
  • Computational personality aims for automatic classification of personality traits against established labels.

Purpose of the Study:

  • To introduce a new vectorial semantics approach for personality assessment.
  • To automatically measure similarities between personality vectors and written texts.
  • To predict personality factors and disorders using computational methods.

Main Methods:

  • Developed a vectorial semantics approach representing personality dimensions and disorders.
  • Utilized a corpus of 2468 student essays, assessed via the five-factor personality model.
  • Calculated similarity between essay vectors and personality vectors to derive personality disorder scores.

Main Results:

  • Personality disorder scores derived from text similarity aligned with established psychological patterns.
  • Accurate prediction of all five personality factors was achieved using the personality vectors.
  • Demonstrated the efficacy of computational models in personality disorder assessment.

Conclusions:

  • The vectorial semantics approach offers a viable computational method for personality assessment.
  • This technique accurately identifies personality factors and disorders from written data.
  • The study highlights the potential of computational personality in psychological research.