improving music mood classification using lyrics, audio and social tags

in a study on music genre classification (Mayer et al., 2008). Besides, combinations of these
feature types are also evaluated in this study and are described in this section as well.
6.2.1 Basic Lyric Features
As a starting point, this research evaluates bag-of-words features with the following types:
1) Content words (Content): all words except function words, without stemming;
2) Content words with stemming (Cont-stem): stemming means combining words with
the same roots;
3) Part-of-speech (POS) tags: such as noun, verb, proper noun, etc. In this research, the
Stanford POS tagger 11 is used to tag each lyric word with one of the 36 unique POS
tags in the Penn Treebank project 12 ;
4) Function words (FW): as opposed to content words, also called “stopwords” in text
information retrieval. The function word list used in this study is the one compiled by
S. Argamon, a well-known scholar in the area of text stylistic analysis 13 .
For each of the feature types, four representation models are compared: 1) Boolean; 2) term
frequency; 3) normalized frequency; and, 4) tfidf weighting. In a Boolean representation model,
each feature value is term presence or absence (one or zero). The term frequency and normalized
frequency models, as their names indicate, use term frequencies and normalized term frequencies
11 http://nlp.stanford.edu/software/tagger.shtml
12 http://www.cis.upenn.edu/~treebank/
13 The function word list can be accessed at http://www.ir.iit.edu/~argamon/function-words.txt
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