Exploring Vocabulary Characteristics across Nine Music Genres: A Corpus-Based Study for Vocabulary Learning

Abstract

The use of music for vocabulary learning has been extensively researched, yet a gap remains in studies focusing on vocabulary across different music genres. This quantitative corpus-based study aims to examine the vocabulary characteristics of nine music genres and their implications for vocabulary learning. To explore these characteristics, lexical profiling, lexical level, CEFR mapping, lexical variation, and lexical density of songs from each genre were examined. Data were collected from Billboard and YouTube songs categorised into nine genres from 2014 to 2023, forming a corpus of 671,051 tokens. The analysis was conducted using AntWordProfiler and VocabProfile. The results revealed that jazz had the highest GSL coverage (91.33%), while hip-hop recorded the lowest (78.88%) but had the highest OWL usage (20.52%). Country and folk (95.20%), jazz (94.74%), and alternative (94.23%) exhibited the greatest use of high-frequency vocabulary, whereas hip-hop displayed the lowest (87.01%) alongside the highest mid-(4.06%) and low-frequency proportions (8.93%). CEFR analysis indicated that country and folk, jazz, alternative, and rock contained mostly A1–A2 vocabulary; R&B/soul, children’s, pop, and electronic genres reflected B1–B2 levels; and hip-hop showed the highest C1–C2 coverage. Jazz (43.44%) and children’s songs (39.73%) demonstrated the greatest lexical variation, while hip-hop (52.88%), children’s (51.80%), and R&B/soul (50.25%) exhibited the highest lexical density. These findings provide guidance for selecting music genres to enhance vocabulary learning at different proficiency levels both inside and outside the classroom.