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We look into the connection between the musical and lyrical content of metalmusic by combining automated extraction of high-level audio features andquantitative 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 LatentDirichlet Allocation (LDA). For a subsample of 503 songs, scores for predictingperceived musical hardness/heaviness and darkness/gloominess were extractedusing audio feature models. By combining both audio feature and text analysis,we (1) offer a comprehensive overview of the lyrical topics present within themetal genre and (2) are able to establish whether or not levels of hardness andother music dimensions are associated with the occurrence of particularly harsh(and other) textual topics. Twenty typical topics were identified and projectedinto a topic space using multidimensional scaling (MDS). After Bonferronicorrection, positive correlations were found between musical hardness anddarkness and textual topics dealing with 'brutal death', 'dystopia', 'archaismsand occultism', 'religion and satanism', 'battle' and '(psychological)madness', while there is a negative associations with topics like 'personallife' and 'love and romance'.

'Warriors of the Word' -- Deciphering Lyrical Topics in Music and Their  Connection to Audio Feature Dimensions Based on a Corpus of Over 100,000  Metal Songs