# Transformer-based approach towards music emotion recognition from lyrics

arXiv: https://arxiv.org/abs/2101.02051 · HTML: https://mlsanity.com/paper/2101.02051/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 7 of 7)

- [Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges](https://mlsanity.com/paper/2406.08809/) (2024)
- [MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition](https://mlsanity.com/paper/2407.06060/) (2024)
- [Modelling Emotion Dynamics in Song Lyrics with State Space Models](https://mlsanity.com/paper/2210.09434/) (2022)
- [Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study](https://mlsanity.com/paper/2501.18633/) (2025)
- [Towards the Generation of Musical Explanations with GPT-3](https://mlsanity.com/paper/2206.08264/) (2022)
- [Language models for longitudinal analysis of abusive content in Billboard Music Charts](https://mlsanity.com/paper/2510.06266/) (2025)
- [A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment](https://mlsanity.com/paper/2606.29273/) (2026)

## Abstract (arXiv)

The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emotion recognition remains under-appreciated in spite of several studies reporting superior performance of music emotion classifiers based on features extracted from lyrics. In this study, we use the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics. Our proposed approach outperforms existing methods for multiple datasets. We used a robust methodology to enhance web-crawlers' accuracy for extracting lyrics. This study has important implications in improving applications involved in playlist generation of music based on emotions in addition to improving music recommendation systems.