Emotion Classification of Lyrics through Summarization by Large Language Models

We propose a method that utilizes a large language model in the task of lyrics emotion classification. We especially employ GPT-4o, which is expected to deliver high performance to conduct emotion classification of lyrics, where we propose a few-shot prompt of GPT-4o for classification into 6 classes. We developed a dataset of 181 lyrics categorized into six classifications to evaluate GPT-4O’s performance. This dataset maintains a reasonable level of validity as it was carefully curated by the first author, then independently reclassified by six annotators, with final classifications determined through majority voting. Performing detailed classification, we achieved over 75% classification performance for the total accuracy. This accuracy was achieved through performing extractive summarization of the lyrics into optimal number of characters. In other words, rather than feeding the collected lyrics directly into GPT-4o, we implemented a two-stage process where GPT-4o first automatically summarizes the lyrics before they are used. Using a confusion matrix, we analyzed the tendencies of classification errors and showed the possibility of improving the total accuracy.