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Suci Pole Mappaita
English Literature Study Program, Faculty of Languages and Literature, Universitas Negeri Makassar, Indonesia

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When Lyrics Speak Beyond Words: Figurative Language in Meghan Trainor’s Songs Rizki Fauzi; Suci Pole Mappaita; Svetlana Urazgaliyeva
Scientific English Vol. 1 No. 2 (2025): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/se.v1i2.121

Abstract

This study examines the use of figurative language in three selected songs by Meghan Trainor—Me Too, Watch Me Do, and Just a Friend to You—to explore how non-literal meaning contributes to emotional expression and identity construction in contemporary pop music. Using a qualitative descriptive method, the lyrics were analyzed to identify figurative forms and interpret their functions. The findings reveal six types of figurative language: hyperbole, metaphor, simile, repetition, generalization, and irony, with hyperbole appearing most frequently across the data. This dominance suggests that Trainor strategically employs exaggeration as a linguistic device to highlight empowerment, humor, confidence, and self-worth. Metaphor and simile further strengthen imagery and cultural association, enabling listeners to visualize meaning more vividly, while irony and generalization emerge particularly in relational contexts to convey emotional tension implicitly. Repetition is used to reinforce lyrical messages, create emphasis, and improve memorability, supporting both poetic and persuasive effects. Overall, the analysis shows that figurative language in Trainor’s lyrics functions not merely as decoration, but as a pragmatic tool that generates implicature, enhances thematic depth, and engages listeners in inferential interpretation. This research contributes to applied linguistics by demonstrating how song lyrics operate as meaningful discourse reflecting social values and personal identity. Future studies are recommended to expand data coverage and integrate multimodal or corpus-based approaches.