Lyrically Speaking: Exploring the Link Between Lyrical Emotions, Themes and Depression Risk

TL;DR

This study analyzes how lyrical themes and emotions in music consumption relate to depression risk, revealing that at-risk individuals prefer lyrics with negative valence and specific themes, which could inform depression assessment and personalized recommendations.

Contribution

It introduces a novel analysis linking lyrical content preferences to depression risk using natural language processing on real-world listening data.

Findings

At-risk individuals prefer songs with low valence and arousal.

Themes of denial, self-reference, and ambivalence are more common among at-risk listeners.

Potential for digital footprint-based depression assessment and personalized music recommendations.

Abstract

Lyrics play a crucial role in affecting and reinforcing emotional states by providing meaning and emotional connotations that interact with the acoustic properties of the music. Specific lyrical themes and emotions may intensify existing negative states in listeners and may lead to undesirable outcomes, especially in listeners with mood disorders such as depression. Hence, it is important for such individuals to be mindful of their listening strategies. In this study, we examine online music consumption of individuals at risk of depression in light of lyrical themes and emotions. Lyrics obtained from the listening histories of 541 Last.fm users, divided into At-Risk and No-Risk based on their mental well-being scores, were analyzed using natural language processing techniques. Statistical analyses of the results revealed that individuals at risk for depression prefer songs with lyrics associated with low valence and low arousal. Additionally, lyrics associated with themes of denial, self-reference, and ambivalence were preferred. In contrast, themes such as liberation, familiarity, and activity are not as favored. This study opens up the possibility of an approach to assessing depression risk from the digital footprint of individuals and potentially developing personalized recommendation systems.