  The task of identifying emotions from a given music track has been an active
pursuit in the Music Information Retrieval (MIR) community for years. Music
emotion recognition has typically relied on acoustic features, social tags, and
other metadata to identify and classify music emotions. The role of lyrics in
music emotion recognition remains under-appreciated in spite of several studies
reporting superior performance of music emotion classifiers based on features
extracted from lyrics. In this study, we use the transformer-based approach
model using XLNet as the base architecture which, till date, has not been used
to identify emotional connotations of music based on lyrics. Our proposed
approach outperforms existing methods for multiple datasets. We used a robust
methodology to enhance web-crawlers' accuracy for extracting lyrics. This study
has important implications in improving applications involved in playlist
generation of music based on emotions in addition to improving music
recommendation systems.
