Abstract
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.
Highlights
Natural Language Processing, K-Nearest Neighbors, Music is said to be the language of emotions and the activity of listening to music is a part of everyday life
Accuracy can be measured by comparing the classes assigned by the algorithm to the classes derived from social tags obtained from last.fm
Songs are classified into emotion categories based on a weighted combination of extracted lyrical and audio features
Summary
Natural Language Processing, K-Nearest Neighbors, Music is said to be the language of emotions and the activity of listening to music is a part of everyday life. Reliable emotion based classification systems are required to facilitate this. The task of music information retrieval is an intriguing one. Research in the field of emotion based music classification has not yielded the best results, especially systems that make use of lyric based analysis.Musical aspects definitely play an important role in deciding the emotion of a song. Most people are able to connect with the words of a song better than its musical features. The words of the song are what truly express the emotions associated with the music, while the musical aspects are generally made to revolve around the lyrical theme
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