Identifying Affective Features of Music Tracks to Determine their Popularity using Machine Learning Approach
Keywords:
Accousticness, Speechiness, Valence, Loudness, Tempo, Machine Learning, Logistic Regression, Decision Tree, KNN, Random Forest, Spotify AppAbstract
Our world of choices for buying products has often been influenced by our friends, peer groups and now the role of technology in building choices cannot be denied. The songs are an integral part of our life and the choice of songs has very often been influenced by our mood, the song’s digital presence, its lyrics, singer, the band and many more appropriate attributes. The present work is a study of a very popular online streaming app i.e. Spotify that has a strong base of music content and is popular among all the age groups alike. The songs have a set of attributes like accousticness, danceability, energy, instrumentalness and many more that impinge the listener’s mind. The collective set of these features when subjected to the machine learning based techniques bring out the best of the best features out of the songs in the database and that creates a popularity chart. The present work depicts the comparative analysis of the various algorithms for the identification of the popularity. The Random Forest based algorithm shows an accuracy of 80.41%, Logistic Regression with 80.15%, KNN with 77.54% and Decision Tree Classifier shows an accuracy of 68.92%.
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