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Hit song prediction, one of the emerging fields in music informationretrieval (MIR), remains a considerable challenge. Being able to understandwhat makes a given song a hit is clearly beneficial to the whole musicindustry. Previous approaches to hit song prediction have focused on usingaudio features of a record. This study aims to improve the prediction result ofthe top 10 hits among Billboard Hot 100 songs using more alternative metadata,including song audio features provided by Spotify, song lyrics, and novelmetadata-based features (title topic, popularity continuity and genre class).Five machine learning approaches are applied, including: k-nearest neighbours,Naive Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Ourresults show that Random Forest (RF) and Logistic Regression (LR) with allfeatures (including novel features, song audio features and lyrics features)outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93AUC, respectively. Our findings also demonstrate the utility of our novel musicmetadata features, which contributed most to the models' discriminativeperformance.

An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features