MUSIC MOOD ANALYZER: A COMPUTATIONAL APPROACH TO EMOTION RECOGNITION IN MUSIC

Abstract

Music plays a vital role in human emotional expression and regulation. With the rapid growth of digital music libraries and streaming platforms, automatic classification of music by mood has become a promising area of research. A Music Mood Analyzer is a computational system that identifies the emotional content of a music track based on features such as rhythm, melody, harmony, timbre, and lyrics. This paper reviews existing methodologies in music mood analysis, explores machine learning and deep learning approaches, and highlights the significance of multimodal analysis integrating audio and textual data. The paper concludes with challenges and future directions in developing more accurate, culturally sensitive, and user-centric mood recognition systems.