![]() It ignores users’ direct feelings about the music, therefore, in the most time, this kind of classification cannot provide users with the most accurate recommendation of what they want. Such a method is convenient, but the disadvantage is also obvious. ![]() Currently, the common classification method is mostly based on the external characteristics of music, such as singer, band, year, album, and so on. In this case, the music classification system begins to play an important role in music discovery for users. As the music consumption paradigm moves towards online streaming services, users have access to increasingly large online music library. With the booming development of network technology, there are more and more online streaming services. Music mood classification becomes one of the best evidence of the music industry’s increasing reliance on artificial intelligence. This article aims to explore how music mood classification system works technically, including the taxonomy and identification of the music mood, as well as the mechanism of machine learning on music mood classification system. Mood classification system improves users’ experience with online music streaming services besides, advertisers could analyze the relationship between music, users’ mood, and their behaviors to re-understand their target consumers emotionally. ![]() Music mood classification is now becoming more and more important for most music streaming services. Explore the Music World: Categorize Music by Mood
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