improving music mood classification using lyrics, audio and social tags

in music psychology. In general, the comparison of the mood categories derived from social tags
to those in psychological models establishes an example of refining and/or adapting theories and
models to better fit the reality of users’ information behaviors.
Text affect analysis has been an active research topic in text mining in recent years (Pang &
Lee, 2008), but has just started being applied to the music domain. Many of the lyric text features
examined in this dissertation have never been formally studied in the context of music mood
classification. Similarly, most of the feature type combinations have never previously been
compared to each other using a common dataset. Thus, this dissertation research advances the
state of text affect analysis in the music domain.
Fusion methods have recently started being used in combining multiple sources in music
classifications, but different fusion methods have rarely been compared on a common dataset.
This dissertation compares two fusion methods, and the result provides suggestions for future
research in music mood classification.
1.4.2 Contributions to Evaluation
As mentioned before, an effective and scalable evaluation approach is much needed in the
music domain. In this dissertation, a large ground truth dataset is built from social tags without
recruiting human assessors (see Section 5.2). The proposed method of deriving ground truth
from social tags can help reduce the prohibitive cost of human assessments and clear the way to
large scale experiments in MIR.
The ground truth dataset built for this study is unique. It contains 5,296 unique songs in 18
mood categories with mood labels given by a number of real-life users. This is one of the largest
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