  In this paper, a method is proposed to detect the emotion of a song based on
its lyrical and audio features. Lyrical features are generated by segmentation
of lyrics during the process of data extraction. ANEW and WordNet knowledge is
then incorporated to compute Valence and Arousal values. In addition to this,
linguistic association rules are applied to ensure that the issue of ambiguity
is properly addressed. Audio features are used to supplement the lyrical ones
and include attributes like energy, tempo, and danceability. These features are
extracted from The Echo Nest, a widely used music intelligence platform.
Construction of training and test sets is done on the basis of social tags
extracted from the last.fm website. The classification is done by applying
feature weighting and stepwise threshold reduction on the k-Nearest Neighbors
algorithm to provide fuzziness in the classification.
