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A standardized personality lexicon for enhancing personalized human-machine interaction.

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This study created a validated personality lexicon for natural language processing (NLP). The lexicon accurately assesses the Big Five personality traits and their facets, advancing computational personality assessment.

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Area of Science:

  • Computational linguistics
  • Psychological sciences
  • Natural Language Processing (NLP)

Background:

  • Personality traits significantly impact language use and social interaction.
  • Existing personality lexicons lack rigorous validation and adaptability to dynamic language environments like social media.
  • Developing personality-informed NLP systems requires robust and validated lexicons.

Purpose of the Study:

  • To construct and empirically validate a new personality lexicon.
  • To improve the alignment between linguistic features and personality traits.
  • To provide a foundation for advanced computational personality assessment.

Main Methods:

  • Derived lexicon from established psychological scales, dictionaries, and literature.
  • Empirically validated the lexicon using real-world participant data.
  • Assessed accuracy across the Big Five personality dimensions and their facets.

Main Results:

  • Achieved high hit rates for all Big Five dimensions (mean = 0.787).
  • Demonstrated high accuracy for the 30 corresponding facets (mean = 0.768).
  • The lexicon shows robust validation and alignment with personality constructs.

Conclusions:

  • The validated lexicon offers a strong foundation for computational personality assessment.
  • Enables applications in personalized NLP, large language models, and mental health prediction.
  • Addresses limitations of existing lexicons in terms of validation and adaptability.