The application of machine learning in analyzing the musical emotional expressions of the Schumann couple

This research aims to explore the application of machine learning technology in music emotion analysis, especially focusing on the works of famous composers Robert Schumann and Clara Schumann in the 19th century. Music emotion analysis has important academic value and broad application prospects in understanding the emotional content of musical works. Through an in-depth analysis of the Schumann couple's works, this study reveals the emotional expression characteristics in the music and the composer's creative style. Leveraging advanced feature engineering and multiple machine learning models, including support vector machines, random forests, and deep neural networks, we evaluate the model's performance on the task of sentiment classification. Experimental results show that deep neural networks perform well in processing complex emotional features, providing a new perspective for further music emotion research. In addition, by decoding the code system in Schumann's musical works, this study not only improves the understanding of his musical language, but also deepens the understanding of the subtle connections between music's emotions. The methodology and findings of this study are of great significance to the fields of music information retrieval, music recommendation systems, and music education.