Sentiment Analysis and Lyrics Theme Recognition of Music Lyrics Based on Natural Language Processing

Abstract

Understanding the emotional nuances and thematic motifs embedded within music lyrics is a captivating endeavour that offers insights into human experiences and societal narratives. In this study, they employ Natural Language Processing (NLP) techniques to conduct sentiment analysis and lyrics theme recognition, unveiling the intricate interplay between language, emotion, and artistic expression in lyrical compositions. Through a comprehensive analysis of diverse music genres and periods, they uncover intriguing patterns and trends in sentiment distribution and thematic content. the results reveal a nuanced distribution of sentiments within the music lyrics corpus, with positive emotions prevailing alongside themes of love, social justice, and personal reflection. Furthermore, temporal trends in sentiment expression highlight the evolving cultural attitudes towards themes of empowerment and positivity over time. Thematic recognition analysis identifies distinct thematic clusters representing prevalent topics across the dataset, reflecting the enduring relevance of certain themes in lyrical discourse. Correlation analysis between sentiment categories and thematic clusters reveals significant alignments between emotional valence and thematic motifs, underscoring the nuanced relationship between sentiment expression and thematic content in music lyrics. Despite certain limitations, the study contributes valuable insights into the emotional and thematic dimensions of music lyrics, paving the way for further interdisciplinary research at the intersection of linguistics, computer science, and musicology.