Investigating the Emergent Reasoning Abilities of Large Language Models in Sentiment Analysis of Codified Music - Assis, Gabriel; Alvarenga, Laura; Moraes, João Vitor de; Azevedo, Lívia de; Paes, Aline (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
The perception of positive or negative sentiment in a piece of music is influenced by musical features such as tempo, notes, chords, and rhythms, collectively shaping its emotional character. This paper examines the ability of Large Language Models (LLMs) to account for these features and infer sentiment from symbolically encoded music, exploring both zero-shot and fine-tuned approaches. Our results show that while LLMs exhibit some capability in processing symbolic representation of musical elements, their ability to associate music with sentiment reliably remains limited. The models struggle to align their predictions with human-assigned labels, with accuracy hovering around 0.6. These results suggest that current text-based approaches may not fully capture the complex interplay between musical structure and emotional expression.