improving music mood classification using lyrics, audio and social tags

Support Vector Machines (SVM), etc. (Sebastiani, 2002). In text categorization evaluation
studies, Naïve Bayes and SVM are almost always considered. Naïve Bayes often serves as a
baseline, while SVM seems to have the top performances (Yu, 2008). In MIR, music
classification studies (mostly on genre classification) often choose KNN and/or decision trees
(C4.5) as baselines to be compared to SVM. Results in both existing music classification
experiments and MIREX classification tasks have shown that the SVM generally, if not always,
outperforms other algorithms (e.g., Hu et al., 2008a; Laurier et al., 2008). As this research needs
to combine both sources of audio and text, SVM is chosen as the classification algorithm.
By design, SVM is a binary classification algorithm. For multi-class classification problems,
a number of SVM have to be learned and each of them predicts the membership of examples to
one class. In order to reduce the chance of overfitting, SVM attempts to find the classification
plane in between two classes and maximizes the margin to either class (Burges, 1998). The data
instances on the margins are called support vectors, while other instances are considered not
contributive to the classification. SVM classifies a new instance by deciding on which side of the
plane the vector of the instance would fall.
An SVM with a linear kernel means that there exists a straight line in a two-dimensional
space that separates one class from another (Figure 4.2). For datasets that are not linearly
separable, higher order kernels are used to project the data to a higher dimensional space where
they become linearly separable. Finding the classification plane involves a complicated
computation of quadratic programming, and thus SVM are more computationally expensive than
Naïve Bayes classifiers. However, SVM are very robust with noisy examples, and they can
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