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<p>Personality, as a stable and coherent set of behavioral and cognitive patterns, significantly influences linguistic expression, emotional regulation, and cognitive functioning. The Big Five personality traits—neuroticism, extraversion, openness, agreeableness, and conscientiousness— are especially relevant for understanding to language use and social interaction, making them foundational for developing of personality-informed natural language processing (NLP) systems. Despite this, existing personality lexicons often lacks rigorous validation, show weak alignment with linguistic features and personality traits, and fail to adapt to dynamic language environments such as social media. This study presents the construction and empirical validation of a personality lexicon derived from established psychological scales, dictionaries, and literature. Validation using real-world participant data yielded high hit rates across all Big Five dimensions (all &gt; 0.70; mean = 0.787) and their 30 corresponding facets (all &gt; 0.60; mean = 0.768). This lexicon provides a robust foundation for advancing computational personality assessment and supports applications in personalized NLP, large language models, and mental health prediction.</p>

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A standardized personality lexicon for enhancing personalized human-machine interaction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Tao Jin,
  • Hui Cai,
  • Xinyi Shi,
  • Xiaomin Kou,
  • Xialian Hu,
  • Hua Zhong,
  • Yan Yang,
  • Jingwen Jiang,
  • Yuchen Li,
  • Wei Zhang

摘要

Personality, as a stable and coherent set of behavioral and cognitive patterns, significantly influences linguistic expression, emotional regulation, and cognitive functioning. The Big Five personality traits—neuroticism, extraversion, openness, agreeableness, and conscientiousness— are especially relevant for understanding to language use and social interaction, making them foundational for developing of personality-informed natural language processing (NLP) systems. Despite this, existing personality lexicons often lacks rigorous validation, show weak alignment with linguistic features and personality traits, and fail to adapt to dynamic language environments such as social media. This study presents the construction and empirical validation of a personality lexicon derived from established psychological scales, dictionaries, and literature. Validation using real-world participant data yielded high hit rates across all Big Five dimensions (all > 0.70; mean = 0.787) and their 30 corresponding facets (all > 0.60; mean = 0.768). This lexicon provides a robust foundation for advancing computational personality assessment and supports applications in personalized NLP, large language models, and mental health prediction.