Language-based personality assessment from life narratives: a focus on model interpretability and efficiency.

Area of Science:

• Computational linguistics
• Psychological assessment
• Artificial intelligence in social sciences

Background:

• Traditional personality assessment relies on questionnaires, limiting depth.
• Existing Natural Language Processing (NLP) research often uses short texts, inadequate for life narratives.
• Long-form narratives offer rich data but challenge standard language models.

Purpose of the Study:

• To develop and evaluate an interpretable and efficient NLP framework for predicting Big Five personality traits from long-form life narratives.
• To address the limitations of current models in handling extensive text data for personality assessment.
• To explore the potential of hybrid NLP models for mental health-related personality analysis.

Main Methods:

• A two-step modeling framework combining transformer-based contextual embeddings (via sliding-window finetuning) and Recurrent Neural Networks (RNNs) with attention mechanisms.
• Extraction of contextual embeddings from life narratives exceeding 2,000 words.
• Utilizing RNNs with attention to capture long-range dependencies and enhance interpretability.

Main Results:

• The proposed hybrid NLP model demonstrated improved prediction accuracy for Big Five personality traits compared to state-of-the-art long-context models.
• The framework achieved greater computational efficiency and enhanced interpretability in personality assessment.
• Successful prediction of personality traits from extensive narrative data was achieved.

Conclusions:

• Interpretable and efficient NLP models show significant potential for personality assessment using life narratives.
• The hybrid approach effectively balances the representational power of transformers with RNNs' sequence-handling capabilities.
• This methodology offers a promising avenue for mental health research through nuanced personality analysis.