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The pursuit of understanding the science behind the success of a song has been a challenge for decades. Hit Song Prediction (HSP), a subfield of Music Information Retrieval, helps artists, labels, and talent scouts predict song performance and streamline market-driven song selection. The purpose of this study is to suggest a new prediction model which has the ability to detect the top 10 songs out of Billboard Hot 100 songs, using a multi-model approach. Using a dataset of 300 charted songs of last 2 years, we have developed a range of ML models including Gradient Boosting, Multi-Layer Perceptron (MLP) and Decision Trees. Analysis incorporated lyrics, audio characteristics, and artist-related data including social media metrics. Results reveal that combining audio, lyrics and social media data is a promising strategy in HSP.

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Leveraging Machine Learning for Song and Artist Success Prediction: A Multimodal Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Ozan Demirel,
  • Tolga Kaya

摘要

The pursuit of understanding the science behind the success of a song has been a challenge for decades. Hit Song Prediction (HSP), a subfield of Music Information Retrieval, helps artists, labels, and talent scouts predict song performance and streamline market-driven song selection. The purpose of this study is to suggest a new prediction model which has the ability to detect the top 10 songs out of Billboard Hot 100 songs, using a multi-model approach. Using a dataset of 300 charted songs of last 2 years, we have developed a range of ML models including Gradient Boosting, Multi-Layer Perceptron (MLP) and Decision Trees. Analysis incorporated lyrics, audio characteristics, and artist-related data including social media metrics. Results reveal that combining audio, lyrics and social media data is a promising strategy in HSP.