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We present an integrated AI framework that predicts song popularity and analyzes sentiment directly from raw audio, using advanced feature extraction and machine learning. The system, implemented as a web application, enables industry professionals to make data-driven decisions based on objective audio indicators. Experiments on real-world data confirm the effectiveness of our approach. We detail the system’s design and discuss future directions for improvement.

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From Sound to Success: An AI Framework for Predicting Music Popularity and Sentiment Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Simona Fioretto,
  • Elio Masciari,
  • Enea Vincenzo Napolitano

摘要

We present an integrated AI framework that predicts song popularity and analyzes sentiment directly from raw audio, using advanced feature extraction and machine learning. The system, implemented as a web application, enables industry professionals to make data-driven decisions based on objective audio indicators. Experiments on real-world data confirm the effectiveness of our approach. We detail the system’s design and discuss future directions for improvement.