improving music mood classification using lyrics, audio and social tags

features achieved better classification performance than using either feature type alone. This
dissertation research first determines the best representation of each feature type and then the
best representations are concatenated with one another.
Specifically, for the basic lyric feature types listed in Table 6.1, the best performing n-grams
and representation of each type (i.e., content words, part-of-speech, and function words) is
chosen and then further concatenated with linguistic and stylistic features. For each of the
linguistic feature types with four representation models, the best representation is selected and
then further concatenated with other feature types. In total, there are eight selected feature types:
1) n-grams of content word (either with or without stemming); 2) n-grams of part-of-speech; 3)
n-grams of function words; 4) GI; 5) GI-lex; 6) ANEW; 7) Affect-lex; and, 8) TextStyle. The
total number of feature type concatenations can be calculated as follows:
8
∑
i= 1
i
C 255
(1)
8
=
where C denotes the combinations of choosing i types from all eight types (i = 1,…,8). All
the 255 feature type concatenations as well as original feature types are compared in the
experiments to find out which lyric feature type or concatenation of multiple types is the best for
the task of music mood classification.
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