@CONFERENCE\{IMM2009-05931,
    author       = "M. K. Petersen and M. M{\o}rup and L. K. Hansen",
    title        = "Sparse but emotional decomposition of lyrics",
    year         = "2009",
    pages        = "31-43",
    booktitle    = "3rd International Workshop on Learning Semantics of Audio Signals (LSAS)",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5931-full.html",
    abstract     = "Both low-level semantics of song texts and our emotional responses can be encoded in words. In order to model how we might perceive the emotional context of songs, we propose a simplified cognitive approach to bottom-up define term vector distances between lyrics and affective adjectives, which top-down constrain the latent semantics according to the psychological dimensions of valence and arousal. Projecting the lyrics and adjectives as vectors into a semantic space using {LSA} latent semantic analysis, their cosine similarities can be mapped as emotions over time. Subsequently we apply a three-way Tucker tensor decomposition to the derived {LSA} matrices, combined with a hierarchical Bayesian automatic relevance determination to find similarities across a selection of songs, and as a result identify two time series dramatic curvatures and three mixtures of affective components, which might function as emotional building blocks for generating the structure in lyrics."
}