# Lyrics Matter: Exploiting the Power of Learnt Representations for Music Popularity Prediction

arXiv: https://arxiv.org/abs/2512.05508 · HTML: https://mlsanity.com/paper/2512.05508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Disentangled Multidimensional Metric Learning for Music Similarity](https://mlsanity.com/paper/2008.03720/) (2020)
- [Music Popularity: Metrics, Characteristics, and Audio-Based Prediction](https://mlsanity.com/paper/1812.00551/) (2018)
- [Direction is what you need: Improving Word Embedding Compression in Large Language Models](https://mlsanity.com/paper/2106.08181/) (2021)
- [It's Only Words And Words Are All I Have](https://mlsanity.com/paper/1901.05227/) (2019)
- [Dance Hit Song Prediction](https://mlsanity.com/paper/1905.08076/) (2019)
- [Lyric document embeddings for music tagging](https://mlsanity.com/paper/2112.11436/) (2021)
- [Unique in what sense? Heterogeneous relationships between multiple types of uniqueness and popularity in music](https://mlsanity.com/paper/2207.12943/) (2022)

## Built on by (most cited first, 1 of 1)

- [APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music](https://mlsanity.com/paper/2605.03395/) (2026)

## Abstract (arXiv)

Accurately predicting music popularity is a critical challenge in the music industry, offering benefits to artists, producers, and streaming platforms. Prior research has largely focused on audio features, social metadata, or model architectures. This work addresses the under-explored role of lyrics in predicting popularity. We present an automated pipeline that uses LLM to extract high-dimensional lyric embeddings, capturing semantic, syntactic, and sequential information. These features are integrated into HitMusicLyricNet, a multimodal architecture that combines audio, lyrics, and social metadata for popularity score prediction in the range 0-100. Our method outperforms existing baselines on the SpotGenTrack dataset, which contains over 100,000 tracks, achieving 9% and 20% improvements in MAE and MSE, respectively. Ablation confirms that gains arise from our LLM-driven lyrics feature pipeline (LyricsAENet), underscoring the value of dense lyric representations.