Music Mood Classification System for Streaming Platform Analysis via Deep Learning Based Feature Extraction
Yu‐Chia Chen, Zih-Ching Chen, Chih‐Hsien Hsia · 2021
The proliferation of smart phones and the Internet has led to the growth of streaming music services. Streaming music services that provide vast libraries of songs have changed the way music lovers listen and leave a digital footprint when they are listening to the music. Music can arouse the emotional feelings of the listeners, thus providing emotional rewards for the listeners and achieving physiological adjustment effect [1]. In general, the study of music mood is based on the melody, rhythm, timbre and other feature of music. However, the lyrics of a song also provides information that helps the listener to have a higher level of understanding of the emotion expressed by the music. In this work, we focus on Spotify, which is the world’s biggest music streaming platform. Using the information retrieved from web crawler, we obtain the data which contains the audio features of the song from Spotify playlist, while analyzing the audio feature and the lyrics through semantic analysis.