Mood classification using lyrics and audio: A case-study in Greek music

Abstract

This paper presents a case-study of the effectiveness of a trained system into classifying Greek songs according to their audio characteristics or/and their lyrics into moods. We examine how the usage of different algorithms, featureset combinations and pre-processing parameters affect the precision and recall percentages of the classification process for each mood model characteristic. Experimental results indicate that the current selection of features offers accuracy results, the superiority of lyrics content over generic audio features as well as potential caveats with current research in Greek language stemming pre-processing methods. © 2012 IFIP International Federation for Information Processing.