The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emotion recognition remains under-appreciated in spite of several studies reporting superior performance of music emotion classifiers based on features extracted from lyrics. In this study, we use the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics. Our proposed approach outperforms existing methods for multiple datasets. We used a robust methodology to enhance web-crawlers' accuracy for extracting lyrics. This study has important implications in improving applications involved in playlist generation of music based on emotions in addition to improving music recommendation systems.

中文翻译：

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基于变压器的基于歌词的音乐情感识别方法

多年来，从给定的音乐曲目中识别情感的任务一直是音乐信息检索（MIR）社区的积极追求。音乐情感识别通常依赖于声学特征，社交标签和其他元数据来识别和分类音乐情感。尽管有几项研究报告了基于从歌词中提取的特征的音乐情感分类器的优越性能，但歌词在音乐情感识别中的作用仍然未被充分认识。在这项研究中，我们使用基于XLNet的基于变压器的方法模型作为基础体系结构，到目前为止，该模型尚未用于基于歌词来识别音乐的情感内涵。我们提出的方法优于针对多个数据集的现有方法。我们使用了一种可靠的方法来增强网络抓取工具提取歌词的准确性。