improving music mood classification using lyrics, audio and social tags

ecommendation task where the algorithm automatically suggested mood or theme descriptors
given social tags associated with a song. As contrast to a classification problem, the problem in
Bischoff et al. (2009a) was a recommendation task where only the first N descriptors were
evaluated (N = 3).
2.3.3 Music Mood Classification Combining Audio and Text
The early work combining lyrics and audio in music mood classification can be traced back
to Yang and Lee (2004) where they used both lyric bag-of-words features and the 182
psychological features proposed in the General Inquirer (Stone, 1966) to disambiguate categories
that audio-based classifiers found confusing. Although the overall classification accuracy was
improved by 2.1%, their dataset was too small (145 songs) to draw any reliable conclusions.
Laurier et al. (2008) also combined audio and lyric bag-of-words features. Their experiments on
1,000 songs in four categories (also from Russell’s model) showed that the combined features
with audio and lyrics improved classification accuracies in all four categories. Yang et al. (2008)
evaluated both unigram and bigram bag-of-words lyric features as well as three methods for
fusing lyric and audio sources on 1,240 songs in four categories (again from Russell’s model)
and concluded that leveraging both lyrics and audio could improve classification accuracy over
audio-only classifiers.
As a very recent work, Bischoff et al. (2009b) combined social tags and audio in music
mood and theme classification. The experiments on 1,612 songs in four and five mood categories
showed that tag-based classifiers performed better than audio-based classifiers while the
combined classifiers were the best. Again, it suggested that combining heterogeneous resources
helped improve classification performances. Instead of concatenating two feature sets like most
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