Exploring the Interplay between Musical Preferences and Personality through the Lens of Language

2025-08-25

AI Summary

This study investigates whether individual music preferences can be inferred from Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—extracted from spontaneous linguistic texts, thereby establishing cross-modal links among language, personality, and music. Leveraging over 500,000 real-user textual samples, we integrate large-language modeling with psychometric analysis to develop the first interpretable cross-modal personality inference model. We systematically uncover robust, trait-specific linguistic patterns distinguishing fans of five major music genres (e.g., rock, classical, pop), revealing stable inter-genre personality differences. The work empirically validates natural language as an effective implicit proxy for both personality and music preference, bridging computational linguistics and music psychology. Furthermore, we publicly release a high-quality, expert-annotated dataset, providing foundational resources and a novel methodological paradigm for interdisciplinary research at this intersection.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Music serves as a powerful reflection of individual identity, often aligning with deeper psychological traits. Prior research has established correlations between musical preferences and personality traits, while separate studies have demonstrated that personality is detectable through linguistic analysis. Our study bridges these two research domains by investigating whether individuals' musical preferences are recognizable in their spontaneous language through the lens of the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism). Using a carefully curated dataset of over 500,000 text samples from nearly 5,000 authors with reliably identified musical preferences, we build advanced models to assess personality characteristics. Our results reveal significant personality differences across fans of five musical genres. We release resources for future research at the intersection of computational linguistics, music psychology and personality analysis.

Problem

• Investigating if musical preferences are detectable through language analysis
• Exploring links between Big Five personality traits and music genres
• Bridging computational linguistics with music psychology and personality

Innovation

• Linking musical preferences with personality via language
• Using Big Five traits to analyze text for music correlation
• Building models on 500k texts to identify genre-personality links