Towards an LLM-based method for quantifying the sexual content in song lyrics
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Abstract (arXiv)
Reggaeton is one of the most widely consumed music genres in the world, and its lyrics are commonly regarded as highly sexualized. This claim rests mostly on qualitative studies and on small-scale quantitative ones. This paper has two goals. First, we present a reproducible method that uses a large language model to quantify thematic content in song lyrics along several independent dimensions. The method is not restricted to sexual content. Second, we apply it to a corpus of 1,259 songs by 12 reggaeton artists released between 2002 and 2025. The analysis covers four topics: a dataset characterization, a per-artist comparison, an analysis of how the dimensions change over time, and a comparison between our sexual-explicitness score and Spotify's own explicit flag. We release the data collection code, the scoring prompt, and the corpus, so that other researchers can replicate the approach or apply it to their own lyrics datasets.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 9 Aug 2026.
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5 of 19 references.
References · 5
- 2017Attention Is All You Needcited by 60,361
- 2023Positive and Risky Message Assessment for Music Productscited by 6
- 2025From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyricscited by 2
- 2025Language models for longitudinal analysis of abusive content in Billboard Music Chartscited by 1
- 2026Fine-Tuning Large Language Models for Automatic Detection of Sexually Explicit Content in Spanish-Language Song Lyricscited by 1
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