improving music mood classification using lyrics, audio and social tags

Fremerey, & Clausen, 2007) or audio, scores and text (McKay & Fujinaga, 2008). However, to
date only a handful of multi-modal studies were on mood classification (Yang & Lee, 2004;
Laurier et al., 2008; Yang et al., 2008), and these studies only used basic text features extracted
from lyrics. Systematic studies on various lyric features and hybrid methods of combining
multiple sources are needed to advance the state of the art in multi-modal music mood
classification.
1.3 RESEARCH QUESTIONS
Aiming at resolving the aforementioned issues in music mood classification, this dissertation
research raises an overarching research question: to what extent can lyrics and social tags help in
categorizing music in regard to mood? It can be divided into the following specific questions.
1.3.1 Identifying Mood Categories from Social Tags
With the birth of Web 2.0, the general public can now post text tags on music pieces and
share these tags with others. The accumulated user tags can yield so called “collective wisdom”
that can augment the value of music itself and create the social context of music seeking and
listening. Specifically, there are two major advantages of social tags. First, social tags are
assigned by real music users in real-life music listening environments, thus they represent the
context of real-life music information behaviors better than labels assigned by human assessors
in laboratory settings. Second, social tags available online are in a large quantity incomparable to
datasets collected by human evaluation experiments, providing a much richer resource of
discovering users’ perspectives.
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