Emotion-Based Song Categorization with Support Vector Machines

In the era of vast digital music libraries, the need for automated song categorization based on emotion has grown substantially for personalized recommendations and efficient content organization. This study introduces a novel approach to this problem by leveraging Support Vector Machines (SVMs) to classify songs into predefined emotional categories. By utilizing a meticulously annotated dataset comprising audio features extracted from songs, we address the challenges posed by the subjective nature of musical emotions. Through rigorous experimentation with various SVM kernels, hyperparameters, and feature engineering techniques, our approach achieves high accuracy in predicting the emotional states of songs, demonstrating its efficacy in enhancing music recommendation systems and mood-based music exploration. This research not only advances the field of music information retrieval but also sheds light on opportunities and future directions in music emotion analysis.