PersonalityLens: Visualizing in-depth analysis for LLM-driven personality insights

Abstract

Large Language Models (LLMs) have demonstrated strong potential for text-based personality assessment and are increasingly adopted by domain experts as assistive tools. Rather than focusing on prediction accuracy, users now prioritize insight-driven analysis, using LLMs to explore large volumes of written and spoken language through simple verbal prompts. However, a gap remains between LLM-detected personality traits and users’ ability to contextualize these outputs within established psychological theories and mechanisms. Existing tools often lack support for multi-level insights and fail to capture the dynamic evolution of traits and facets over time, limiting deeper analysis. To address this, we propose PersonalityLens, a visual analysis tool designed to enhance insight discovery in personality analysis. Our design is informed by a comprehensive requirements analysis with domain experts and supports: (1) in-depth exploration of detected traits and their corresponding utterances, supporting insights at varying levels of granularity, (2) exploration of how personality traits and facets dynamically evolve in finer contexts over time, (3) alignment of traits and facets with psychological theories. We present two complementary case studies — one based on fictional TV dialogue and the other on therapeutic interactions — demonstrating PersonalityLens’s adaptability to diverse analytic goals and contexts. A qualitative think-aloud user study shows that PersonalityLens supports context-aware interpretation and insight discovery. Building on these findings, we outline design implications to inspire future research and enhance psychotherapy tools with integrated personality analysis for mental health support.