Harmonizing sentiments: Analyzing user reviews of Spotify through sentiment analysis

Evaristus Didik Madyatmadja; Felix Felix; I. Gusti Ketut Edrick; Joseph William Indarto; David Jumpa Malem Sembiring

doi:10.24294/jipd.v8i9.7101

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Harmonizing sentiments: Analyzing user reviews of Spotify through sentiment analysis

Evaristus Didik Madyatmadja

Felix Felix

I. Gusti Ketut Edrick

Joseph William Indarto

David Jumpa Malem Sembiring

Journal of Infrastructure Policy and Development 2024, 8(9), 7101; https://doi.org/10.24294/jipd.v8i9.7101

Submitted：12 Jun 2024

Accepted：22 Jun 2024

Published：10 Sept 2024

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Abstract

This research investigates the sentiment of user reviews on Spotify, with a particular focus on the Indonesian market, leveraging advanced sentiment analysis techniques. We employed three prominent classification models—Naïve Bayes, Support Vector Machine (SVM), and Random Forest—to analyze a dataset of 14,296 user reviews extracted from the Google Play Store and App Store. These findings reveal that the SVM model achieved the highest performance, with an F1-score of 0.875 and an accuracy of 0.874, outperforming Naïve Bayes and Random Forest, which scored accuracies of 0.857 and 0.856 respectively. These results highlight not only the significance of this research which offers valuable contributions to the broader academic discourse on digital marketing, sentiment analysis, and consumer behavior. Additionally, it also showcases the robustness and superior performance of SVM and Random Forest in various sentiment analysis contexts. This study not only provides valuable insights for Spotify’s future development strategies but also contributes to the broader academic discourse on sentiment analysis and machine learning model performance in digital marketing. By highlighting the efficacy of specific models, this research underscores the importance of model selection in sentiment analysis, paving the way for more accurate and effective sentiment analysis applications in the music streaming industry.

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References

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