Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing
SoundSoar applies machine learning to predict Spotify song popularity trends—“up,” “down,” or “stable”—using engineered audio features and historical data. Among eight tested classifiers, the MLP achieved 97.0% accuracy, while ensemble models like Random Forest consistently performed well (90.0%–91...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/139012 |
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| author | Victoria Grasso Andreas Marpaung |
| author_facet | Victoria Grasso Andreas Marpaung |
| author_sort | Victoria Grasso |
| collection | DOAJ |
| description |
SoundSoar applies machine learning to predict Spotify song popularity trends—“up,” “down,” or “stable”—using engineered audio features and historical data. Among eight tested classifiers, the MLP achieved 97.0% accuracy, while ensemble models like Random Forest consistently performed well (90.0%–91.0%). By leveraging a tailored dataset and diverse models, this approach improves upon broader trend prediction methods. Despite challenges from Spotify API changes in late 2024, our findings validate machine learning’s role in music trend forecasting and set the stage for future enhancements, such as custom sound analysis and cross-platform models.
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| format | Article |
| id | doaj-art-c35f0b51945249c8a93423efcd57d195 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-c35f0b51945249c8a93423efcd57d1952025-08-20T01:49:59ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.139012Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing Victoria Grasso0Andreas Marpaung1Full Sail UniversityFull Sail University SoundSoar applies machine learning to predict Spotify song popularity trends—“up,” “down,” or “stable”—using engineered audio features and historical data. Among eight tested classifiers, the MLP achieved 97.0% accuracy, while ensemble models like Random Forest consistently performed well (90.0%–91.0%). By leveraging a tailored dataset and diverse models, this approach improves upon broader trend prediction methods. Despite challenges from Spotify API changes in late 2024, our findings validate machine learning’s role in music trend forecasting and set the stage for future enhancements, such as custom sound analysis and cross-platform models. https://journals.flvc.org/FLAIRS/article/view/139012Machine Learningagent-based modelFeature EngineeringClassification |
| spellingShingle | Victoria Grasso Andreas Marpaung Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing Proceedings of the International Florida Artificial Intelligence Research Society Conference Machine Learning agent-based model Feature Engineering Classification |
| title | Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing |
| title_full | Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing |
| title_fullStr | Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing |
| title_full_unstemmed | Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing |
| title_short | Classifying Spotify Song Trends with SoundSoar: Machine Learning Insights for Content Creation and Marketing |
| title_sort | classifying spotify song trends with soundsoar machine learning insights for content creation and marketing |
| topic | Machine Learning agent-based model Feature Engineering Classification |
| url | https://journals.flvc.org/FLAIRS/article/view/139012 |
| work_keys_str_mv | AT victoriagrasso classifyingspotifysongtrendswithsoundsoarmachinelearninginsightsforcontentcreationandmarketing AT andreasmarpaung classifyingspotifysongtrendswithsoundsoarmachinelearninginsightsforcontentcreationandmarketing |