Classification of Songs in Spanish with LLMs: An Analysis of the Construction of a Dataset Through Classification

Abstract

Songs are a powerful means of expressing emotions through melody and lyrics. This study focuses on understanding and classifying the emotions present in songs, including positive, negative, and neutral emotions. Using large-scale language models (LLMs) such as BERT, RoBERTa, and DistilBERT, two datasets related to song emotions were analyzed. Despite their effectiveness in capturing emotions in lyrics, these models faced notable challenges. The variability in sentence length in musical data hindered generalization, and the imbalance in the distribution of emotions in the datasets affected the models’ ability to address the issue. To overcome these limitations, the creation of a third dataset specifically designed for song sentiment analysis was proposed. This new dataset addressed sentence length challenges by providing examples of song lyrics of varying lengths, enabling more effective model training. Furthermore, data imbalance was addressed through careful sample selection, representing a wide range of emotions in songs. The third dataset underwent classification using large-scale language models, achieving promising results. The accuracy metric reached an impressive 96.34%, highlighting the effectiveness of this approach in song sentiment analysis. This study underscores the importance of understanding emotions in songs and offers practical solutions to enhance the capabilities of language models in this task.