How individuals perceive music is inﬂuenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information inﬂuences how we experience music, as does the songs´ text and its sound.
Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, may thus be used to improve classiﬁcation or similarity ranking of music. Song lyrics exhibit speciﬁc properties different from traditional text documents - many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use `slang´ language or
differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word / verse length, and the amount of repetative text. In this paper, we present a novel set of features developed for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classiﬁcation on a test collection in order to demonstrate our analysis.