improving music mood classification using lyrics, audio and social tags
improving music mood classification using lyrics, audio and social tags
improving music mood classification using lyrics, audio and social tags
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systems <strong>and</strong> the <strong>audio</strong>-only system was statistically significant, but the difference between<br />
hybrid systems <strong>and</strong> the lyric-only system was not statistically significant.<br />
Figure 7.3 shows the system accuracies across individual <strong>mood</strong> categories for the BEST<br />
lyric feature set where the categories are in descending order of the number of songs in each<br />
category.<br />
Figure 7.3 System accuracies across individual categories for the BEST lyric feature set<br />
Figure 7.3 reveals that system performances become more erratic <strong>and</strong> unstable after the<br />
category “cheerful.” Those categories to the right of “cheerful” have fewer than 142 positive<br />
examples. This suggests that the systems are vulnerable to the data scarcity problem. For some of<br />
the smaller categories, system performances were even lower than baseline performance (50%<br />
for binary <strong>classification</strong>). This is a somewhat expected result as the lengths of the feature vectors<br />
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