# Towards Estimating Personal Values in Song Lyrics

arXiv: https://arxiv.org/abs/2408.12694 · HTML: https://mlsanity.com/paper/2408.12694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Annotation Sensitivity: Training Data Collection Methods Affect Model Performance](https://mlsanity.com/paper/2311.14212/) (2023)
- [Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection](https://mlsanity.com/paper/2308.12885/) (2023)
- ["More Than Words": Linking Music Preferences and Moral Values Through Lyrics](https://mlsanity.com/paper/2209.01169/) (2022)

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

- [The Value of Nothing: Multimodal Extraction of Human Values Expressed by TikTok Influencers](https://mlsanity.com/paper/2501.11770/) (2025)

## Abstract (arXiv)

Most music widely consumed in Western Countries contains song lyrics, with U.S. samples reporting almost all of their song libraries contain lyrics. In parallel, social science theory suggests that personal values - the abstract goals that guide our decisions and behaviors - play an important role in communication: we share what is important to us to coordinate efforts, solve problems and meet challenges. Thus, the values communicated in song lyrics may be similar or different to those of the listener, and by extension affect the listener's reaction to the song. This suggests that working towards automated estimation of values in lyrics may assist in downstream MIR tasks, in particular, personalization. However, as highly subjective text, song lyrics present a challenge in terms of sampling songs to be annotated, annotation methods, and in choosing a method for aggregation. In this project, we take a perspectivist approach, guided by social science theory, to gathering annotations, estimating their quality, and aggregating them. We then compare aggregated ratings to estimates based on pre-trained sentence/word embedding models by employing a validated value dictionary. We discuss conceptually 'fuzzy' solutions to sampling and annotation challenges, promising initial results in annotation quality and in automated estimations, and future directions.