Music Emotion Recognition from Lyrics: A Comparative Study
We present a study on music emotion recognition from lyrics. We start from a dataset of 764 samples (audio+lyrics) and perform feature extraction using several natural language processing techniques. Our goal is to build classifiers for the different datasets, comparing different algorithms and using feature selection. The best results (44.2% F-measure) were attained with SVMs. We also perform a bi-modal analysis that combines the best feature sets of audio and lyrics.The combination of the best audio and lyrics features achieved better results than the best feature set from audio only (63.9% F-Measure against 62.4% F-Measure).
music emotion recognition, lyrics, multi-modal fusion, natural language processing, machine learning
music emotion recognition
MOODetector: A System for Mood-based Classification and Retrieval of Audio Music
6th International Workshop on Machine Learning and Music, September 2013