Enhancing Music Emotion Classification Using Multi-Feature Approach

Abstract

Emotions are a fundamental aspect of human expression, and music lyrics are a rich source of emotional content. Understanding the emotions conveyed in lyrics is crucial for a variety of applications, including music recommendation systems, emotion classification, and emotion-driven music composition. While extensive research has been conducted on emotion classification using audio or combined audio-lyrics data, relatively few studies focus exclusively on lyrics. This gap highlights the need for more focused research on lyric-based emotion classification to better understand its unique challenges and potentials. This paper introduces a novel approach for emotion classification in music lyrics, leveraging a combination of natural language processing (NLP) techniques and dimension reduction methods. Our methodology systematically extracts and represents the emotional features embedded within the lyrics, utilizing a diverse set of NLP techniques and integrating new features derived from various emotion lexicons and text analysis. Through extensive experimentation, we demonstrate the effectiveness of our approach, achieving significant improvements in accurately classifying the emotions expressed in music lyrics. This study underscores the potential of lyric-based emotion analysis and provides a robust framework for further research in this area.