SentiSpotMusic: a music recommendation system based on sentiment analysis
Eva Sarin, Srishti Vashishtha, Megha Megha, Simran Kaur · 2022
Transformation in digital media has modified different aspects of life, as well as the music industry and listener’s listening habits. The unfolding of handy electronic devices and browser music listening services has eased the chance for accessing colossal choice of music. However, this access ends up in the customer's downside of selecting the correct music for an explicit state of affairs or mood. The user is usually flooded with many songs while selecting the music. The present applications are not providing selection of music based on sentiments. The music service suppliers provide predefined playlists for different categories. But, the matter with the generated lists concerns them being not adaptive to modern and latest user conditions. Currently, not much investigation has been done in recommending songs based on the mood of users. SentiSpotMusic: a music recommendation system framework based on sentiments has been proposed in this paper using tableau dashboard and Spotify dataset.