improving music mood classification using lyrics, audio and social tags

Except for the combination of ANEW and TextStyle, all of the other top performing feature
combinations shown in Table 6.5 are concatenations of four or more feature types, and thus have
very high dimensionality. In contrast, ANEW+TextStyle has only 37 dimensions, which is
certainly a lot more efficient than the others. On the other hand, high dimensionality provides
room for feature selection and reduction. Indeed, a previous study of the author (Hu et al., 2009a)
applied three feature selection methods on basic unigram lyric features (i.e., F-Score, SVM score
and language model comparisons) and showed improved performances. It is a future research
direction to investigate feature selection and reduction for feature combinations with high
dimensionality.
Except for ANEW+TextStyle, all other top performing feature concatenations contain the
combination of “Content,” “FW,” “GI,” and “TextStyle.” The relative importance of the four
individual feature types can be revealed by comparing the combinations of any three of the four
types. As shown in Table 6.6, the combination of FW + GI + TextStyle performed the worst.
Together with the fact that Content performed the best among all individual feature types, it is
safe to conclude that content words are still very important in the task of lyric mood
classification.
Table 6.6 Performance comparison of “Content,” “FW,” “GI,” and “TextStyle”
Type
Accuracy
Content+FW+TextStyle 0.632
Content+FW+GI 0.631
Content+GI+TextStyle 0.624
FW+GI+TextStyle 0.619
83