improving music mood classification using lyrics, audio and social tags

There are several operational systems focused on text categorization according to affect.
Subasic and Huettner (2001) manually constructed a word lexicon for each affect category
considered in their study, and classified documents by comparing the average scores of terms in
the affect categories. A more complex approach was taken by Liu, Lieberman, and Selker (2003)
which was based on common sense knowledge, due to the assumption that common sense is
important for affect interpretation.
As lyrics are a special genre quite different from daily life documents, a common sense
knowledge base may not work for lyrics; neither do word lexicons built for other genres of
documents. While manually building a lexicon is very labor-intensive, methods on automatic
lexicon induction have been proposed. Pang and Lee (2008) summarized such methods and
categorized them into two groups: unsupervised and supervised. The three feature selection
methods applied to lyrics in Hu et al. (2009a) (e.g., language model comparison, F-score feature
ranking, and SVM feature ranking) are vivid examples of supervised lexicon induction.
Unsupervised methods start from a few seed words for which the affect is already known, and
then propagate the labels of the seed words to words that co-occur with them in a text corpus, to
synonyms, and/or to words that co-occur with them in other resources like WordNet. For
instance, Turney (2002) proposed the joint use of mutual information and co-occurrence in a
general corpus with a small set of seed words.
There have been interesting studies on the affective aspect of text in the context of weblogs
(Nicolov, Salvetti, Liberman, & Martin, 2006). Most of them still used bag-of-words features of
all words or words in specific POSs (mostly adjectives and nouns). Among them, Mihalcea and
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