Music emotion classification for Turkish songs using lyrics

Music has grown into an important part of people's daily lives. As we move further into the digital age in which a large collection of music is being created daily and becomes easily accessible renders people to spend more time on activities that involve music. Consequently, the form of music retrieval is changed from catalogue based searches to searches made based on emotion tags in order for easy and effective musical information access. In this study, it is aimed to generate a model for automatic recognition of the perceived emotion of songs with the help of their lyrics and machine learning algorithms. For this purpose, first 300 songs are selected and annotated by human taggers with respect to their perceived emotions. Thereafter, Unigram, Bigram and Trigram word features are extracted from song lyrics after performing text preprocessing where stemming of the Turkish words is an essential part. Then, term by document matrices are created where term frequencies and tf-idf scores are considered as representations for the indices. Five different classification algorithms are fed with these matrices in order to find the best combination that achieves the highest accuracy results where recall and precision values are used as comparison metrics. As a result, best accuracy results are obtained by using Multinomial Naive Bayes classifier where Unigram features are used to create the term by document matrix. In this setting, Unigram features are stemmed by Zemberek Long stemming method, and the index representation is chosen as term frequency. For this combination, obtained recall and precision values are 43.7 and 46.9, respectively.