improving music mood classification using lyrics, audio and social tags

The patterns shown in this figure are similar to those found in Russell’s model as shown in
Figure 2.2: 1) Categories placed together are intuitively similar; 2) Categories at opposite
position represent contrast moods; and, 3) The horizontal and vertical dimensions correspond to
valence and arousal respectively. Taken together, these similarities indicate that these 18 mood
categories fit well with Russell’s mood model which is the most commonly used model in MIR
mood classification research. In addition, it is interesting that both Figure 5.2 and Figure 3.3
show there are more sad songs than happy songs. Although this observation looks intuitively
reasonable (e.g., most poems are sad rather than happy), further validation is needed from
musicology and/or music psychology.
The full dataset comprises 5,296 unique songs, including positive and negative examples.
This number is much smaller than the total number of examples in all categories (which is
12,980) because categories often share samples. The decomposition of genres in this dataset is
shown in Table 5.5 from which we can see most of the songs in this dataset are pop music.
Table 5.5 Genre distribution of songs in the experiment dataset (“Other” includes genres
occurring very infrequently such as “World,” “Folk,” “Easy listening,” and “Big band”)
Genre No. of songs Genre No. of songs Genre No. of songs
Rock 3,977 Reggae 55 Oldies 15
Hip Hop 214 Jazz 40 Other 35
Country 136 Blues 40 Unknown 564
Electronic 94 Metal 37 TOTAL 5,296
R & B 64 New Age 25
To have a clear look at the relationship between mood and genre, Table 5.6 summarizes how
the 6,490 positive examples distribute across different genres and moods. Although for this
dataset all moods are dominated by Rock songs, there are still observations that comply with
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