Hit Song Prediction Based on Early Adopter Data and Audio Features
This provides a strategy for the music industry to assess hit potential and support investment decisions, though it is incremental as it builds on existing prediction methods with new features.
The research tackled the problem of predicting hit songs by developing models that use audio data and a novel social media listening behavior feature, showing that models based on early adopter behavior perform well in predicting top 20 dance hits.
Billions of USD are invested in new artists and songs by the music industry every year. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. A number of models were developed that use both audio data, and a novel feature based on social media listening behaviour. The results show that models based on early adopter behaviour perform well when predicting top 20 dance hits.