Automatic Mood Classification Using TF*IDF Based on Lyrics

This paper presents the outcomes of research into using lingual parts of music in an automatic mood classification system. Using a collection of lyrics and corresponding user-tagged moods, we build classifiers that classify lyrics of songs into moods. By comparing the performance of different mood frameworks (or dimensions), we examine to what extent the linguistic part of music reveals adequate information for assigning a mood category and which aspects of mood can be classified best. Our results show that word oriented metrics provide a valuable source of information for automatic mood classification of music, based on lyrics only. Metrics such as term frequencies and tf*idf values are used to measure relevance of words to the different mood classes. These metrics are incorporated in a machine learning classifier setup. Different partitions of the mood plane are investigated and we show that there is no large difference in mood prediction based on the mood division. Predictions on the valence, tension and combinations of aspects lead to similar performance.