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Emotional Misalignment in Contemporary Music: Systematic Patterns of Audio-Lyrical Contrast Across Genres
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Resumo(s)
This thesis investigates emotional alignment between audio features and lyrical sentiment in contemporary popular music, addressing a gap in music emotion research through a novel dual-modality approach. The study analyzed 1,080 songs across 12 genres using K-means clustering of Spotify audio features and GPT-4 lyrical emotion classification, identifying three emotional clusters: Aggressive/Intense, Sad/Calm, and Happy/Upbeat. Key findings reveal that emotional misalignment is the norm rather than the exception: only 46% of songs showed audio-lyrical alignment, while 54% exhibited systematic emotional contrast. Statistical analysis confirmed significant relationships between modalities (χ2 = 99.45, p < 0.001) but slight practical agreement (κ = 0.164), indicating systematic yet divergent patterns. Genre analysis revealed distinct strategies: hip-hop featured angry lyrics with happy audio (55% mismatches), jazz combined happy lyrics with sad audio (92% mismatches), and metal paired sad lyrics with angry audio (66% mismatches). The research demonstrates that emotional misalignment represents intentional artistic choices creating irony, amplification, or accessibility rather than anomalies. These findings have significant implications for music recommendation systems, therapeutic applications, and AI-generated music, suggesting need for nuanced approaches accounting for multi-modal complexity.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Music emotion analysis Audio features Lyrical sentiment Multi-modal analysis Emotional alignment Machine learning SDG 3 - Good health and well-being SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure