In this paper, we propose using AdaBoost with decision trees to implement music emotion classification (MEC) from song lyrics as a more appropriate alternative to the conventional SVMs. Traditional text categorizations methods using bag-of-words features and machine learning methods such as SVM do not perform well on MEC from lyrics because lyrics tend to be much shorter than other documents. Boosting builds on a lot of weak classifiers to model the presence or absence of salient phrases to make the final classification. Our accuracy reached an average of 74.12% on a dataset consisting of 3766 songs with 14 emotion categories, compared to an average of 69.72% accuracy using the well-known SVM classification, with statistical significant improvement.
展开▼
