Abstract: Mood and emotion play an important role when it comes to choosing musical tracks to listen to. In the field of music information retrieval and recommendation, emotion is considered contextual information that is hard to capture. Modern day entertainment and music streaming has largely been dependent on digital technologies. People prefer subscription based online services for buying physical copies of the music albums. online streaming services like Spotify, apple iTunes, google music offer great services to the listener with ease. However, drawbacks to these systems includes long delays in payouts for the artists, lack of transparency, confusing payments and licensing terms and so on. In order to give solution to these drawbacks the proposed system is an interface which is used between the user (fans or melomaniacs) and the independent musicians without involving the third parties and thereby satisfying the user by fetching them the songs of their mood by using the sentimental analysis. we have proposed it as a collaborative work by which the artists gets benefited as well as the user gets satisfied with the help of machine learning techniques.
Keywords: Melomaniacs, Machine Learning, Sentimental Analysis,
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DOI:
10.17148/IJARCCE.2022.11518
