Mood Classification with Lyrics and ConvNets

Abstract

The paper presents research outcomes of classifying music into moods and provides an end-to-end, open source pipeline for mood classification using lyrics. It explores techniques that classify music using audio features and lyrics using various natural language processing methods and machine learning. The paper performs a comparative study across different classification models and mood frameworks. The linguistic aspects of lyrics are explored and are used as features for classification methods to understand what model classifies mood in the most adequate manner. The results show how lyrics are a valuable information source for classification of music. Term-frequency/inverse-document frequency and word embeddings are explored to connect words to mood classes. Various machine learning and deep learning classifiers are tested across different arrangements of the mood labels. The paper demonstrates that models which learn from lyrics using current methods of natural language processing using deep learning demonstrate higher levels of accuracy. Our final model achieves an accuracy of 71%.