  We look into the connection between the musical and lyrical content of metal
music by combining automated extraction of high-level audio features and
quantitative text analysis on a corpus of 124.288 song lyrics from this genre.
Based on this text corpus, a topic model was first constructed using Latent
Dirichlet Allocation (LDA). For a subsample of 503 songs, scores for predicting
perceived musical hardness/heaviness and darkness/gloominess were extracted
using audio feature models. By combining both audio feature and text analysis,
we (1) offer a comprehensive overview of the lyrical topics present within the
metal genre and (2) are able to establish whether or not levels of hardness and
other music dimensions are associated with the occurrence of particularly harsh
(and other) textual topics. Twenty typical topics were identified and projected
into a topic space using multidimensional scaling (MDS). After Bonferroni
correction, positive correlations were found between musical hardness and
darkness and textual topics dealing with 'brutal death', 'dystopia', 'archaisms
and occultism', 'religion and satanism', 'battle' and '(psychological)
madness', while there is a negative associations with topics like 'personal
life' and 'love and romance'.
