improving music mood classification using lyrics, audio and social tags

different than those selected by SVM. In addition, novel and high level audio features have been
recently proposed such as “danceability” (Laurier et al., 2008) which measures how likely
listeners would dance with the music. Combining lyrics with those new audio features may
further improve classification performance.
9.3.3 Enrich Music Mood Theories
This study has identified mood categories from social tags and presented an empirical and
real-life case for the reference of music psychologists. In the future, the author will strive to
identify more patterns in people’s music listening behaviors from social media data, find
connections between these patterns and music psychology theories, and offer suggestions and
insights to music psychology research.
An example of this type of research question would be the degree of moods in individual
music pieces. In this research, the membership to each mood category is binary. That is, a song
either belongs to a category or not. In the reality, songs often show a combination of moods with
certain degrees, such as “a mostly calm song with a bit of sadness.” Besides, other related topics
include the taxonomy of degrees, definition of correctness, differentiation of two types of errors
(i.e., false positive and false negative), etc. Once discovered, the findings will help enrich and
extend the theories on music mood.
9.3.4 Music of Other Types and in Other Cultures
Due to data availability, this study focuses on popular English songs. Other types of music
such as classical music have long been studied by music psychologists. When data become
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