We present a novel approach for analysing and classifying lyrics, experimenting both with ngram models and more sophisticated features that model different dimensions of a song text, such as vocabulary, style, semantics, orientation towards the world, and song structure. We show that these can be combined with n-gram features to obtain performance gains on three different classification tasks: genre detection, distinguishing the best and the worst songs, and determining the approximate publication time of a song.
REF categorized lyrics features into five main classes: vocabulary, style, semantics, orientation and song structure, and used them for music genre classification.
Lyrics-based Analysis and Classification of Music
COLING
Computer Science Psychology