# It's Only Words And Words Are All I Have

arXiv: https://arxiv.org/abs/1901.05227 · HTML: https://mlsanity.com/paper/1901.05227/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Are Word Embedding Methods Stable and Should We Care About It?](https://mlsanity.com/paper/2104.08433/) (2021)
- [Large scale analysis of gender bias and sexism in song lyrics](https://mlsanity.com/paper/2208.02052/) (2022)
- [Lyrics Matter: Exploiting the Power of Learnt Representations for Music Popularity Prediction](https://mlsanity.com/paper/2512.05508/) (2025)

## Abstract (arXiv)

The central idea of this paper is to demonstrate the strength of lyrics for music mining and natural language processing (NLP) tasks using the distributed representation paradigm. For music mining, we address two prediction tasks for songs: genre and popularity. Existing works for both these problems have two major bottlenecks. First, they represent lyrics using handcrafted features that require intricate knowledge of language and music. Second, they consider lyrics as a weak indicator of genre and popularity. We overcome both the bottlenecks by representing lyrics using distributed representation. In our work, genre identification is a multi-class classification task whereas popularity prediction is a binary classification task. We achieve an F1 score of around 0.6 for both the tasks using only lyrics. Distributed representation of words is now heavily used for various NLP algorithms. We show that lyrics can be used to improve the quality of this representation.