Using Generative Artificial Intelligence to Identify Themes of Basic Psychological Needs in Popular Song Lyrics

Abstract

Music listening is a universal human behaviour, increasingly recognised for its association with well-being. The mechanisms underlying how music listening influences well-being remain unclear, especially in terms of broader motivational processes. Utilising a self-determination theory (SDT) framework, we examined the extent to which popular song lyrics represented themes of basic psychological needs (BPN). Our corpus comprised the top 10 songs of the Australian Recording Industry Association’s top 100 singles chart for the period 1988–2023, resulting in 360 unique songs. Lyrics were analysed through deductive content analysis using a researcher-created application, MuseAI , which employs large language model platforms using custom media input. Our findings indicated the representation of all three BPNs across the pop song lyric corpus. Relatedness themes occurred at the highest frequency, followed by autonomy and competence, respectively. Need satisfaction occurred more frequently than need frustration. As these songs were those most listened to by audiences across more than three decades, our findings suggest that song lyrics frequently represent themes of BPN, which may help explain the motivational appeal of music listening. This exploratory research provides initial theoretical support for further investigation of music listening engagement utilising an SDT framework.