NLP-BASED MUSIC LYRIC ANALYSIS IN EDUCATION

Abstract

The intersection of computational linguistics, affective computing and education innovation is the Lyric analysis through Natural Language Processing (NLP). Using deep learning networks CNNs to identify rhythmic patterns, Transformers to map the song lyrics to contextual emotions, and GANs to add metaphors to the lyrics the framework converts the lyrics of the songs into computational-affective products. The research combines quantitative modeling and qualitative pedagogy, making it possible to visualize emotions, track Valence–Arousal-Dominance (VAD) and detect metaphors, which can be used in support of language learning and emotional literacy. A multilingual collection of curated lyric corpus of pop, folk, and educative songs was evaluated through the use of BERT-based sentiment models and topic clustering. Empirical findings indicate that the F1-score of emotion classification is 0.87 and that there are significant pedagogical gains such as 2834% enhancement in student comprehension, empathy and engagement. There was high adoption (87%) and improved interpretive dialogue and inclusivity of teachers who used AI-assisted dashboards. These were supported by donut chart representations of emotional distribution, engagement and teacher satisfaction. The framework also extends linguistic and cultural knowledge as well as reinvents AI as a collaborative co-creator in education and enables reflective, empathetic and data-informed learning experiences. Future directions Multimodal lyric analysis (text and audio) Multimodal adaptive learning systems based on cognitive profiles Culturally balanced corpora that maintain regional diversity Future directions Multimodal lyric analysis (text and audio) Multimodal adaptive learning systems based on cognitive profiles Culturally balanced corpora that maintain regional diversity Lyric analysis using NLP therefore creates a platform of emotionally intelligent, culturally inclusive and AI augmented learning.