improving music mood classification using lyrics, audio and social tags

then compared to the feature concatenation hybrid system, as well as the systems based on lyriconly
and audio-only.
7.2.2 Best Hybrid Method
Since the best lyric feature set was Content + FW + GI + ANEW + Affect-lex + TextStyle
(denoted as “BEST” thereafter), and the second best feature set, ANEW + TextStyle was very
interesting; each of the two lyric feature sets was combined with the audio-based system
described in Section 7.1. For the late fusion method, an experiment was conducted to determine
the α value that led to the best performance. The results are shown in Figure 7.1.
Figure 7.1 Effect of α value in late fusion on average accuracy
The highest average accuracy was achieved when α = 0.5 for both lyric feature sets, that is
when the lyric-based and audio-based classifiers got equal weights. This indicates both lyrics and
audio are equally important for maximizing the advantage of the late fusion hybrid systems.
Table 7.1 presents the average accuracies of systems using the two hybrid methods, as well as
the result of a statistical test on system performances. The method of late fusion outperformed
92