The Impact of Artificial Intelligence on Sentiment Analysis Detection in Music Reviews

This study aims to perform sentiment and content analysis of Spotify user reviews using machine learning and deep learning methods. The goal is to better understand users' experiences and satisfaction. The study employs various machine learning and deep learning techniques to identify the emotional tendencies in user reviews and analyze the relationship between these tendencies and content features. The performance of these methods is evaluated using various metrics such as accuracy, precision, recall, and F1-score. By identifying the strengths and weaknesses of each method, the study determines which techniques are more effective in specific situations. The results provide valuable insights for improving the quality of music streaming services and enhancing user experience. This study aims to help service providers increase user satisfaction by gaining a better understanding of user feedback. Additionally, these analyses are expected to provide valuable data for future improvements in music streaming services. Thus, it will be possible to continuously improve user experiences and enhance service quality.

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Publication Details

Journal
Balkan Journal of Electrical and Computer Engineering
Published
2025-09-30
DOI
https://doi.org/10.17694/bajece.1492287
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

The Impact of Artificial Intelligence on Sentiment Analysis Detection in Music Reviews

Buğra Kağan Kayhan, Murat Şimşek
Balkan Journal of Electrical and Computer Engineering
Traffic Prediction and Management Techniques
article

The Impact of Artificial Intelligence on Sentiment Analysis Detection in Music Reviews

Buğra Kağan Kayhan, Murat Şimşek
article en

Abstract

This study aims to perform sentiment and content analysis of Spotify user reviews using machine learning and deep learning methods. The goal is to better understand users' experiences and satisfaction. The study employs various machine learning and deep learning techniques to identify the emotional tendencies in user reviews and analyze the relationship between these tendencies and content features. The performance of these methods is evaluated using various metrics such as accuracy, precision, recall, and F1-score. By identifying the strengths and weaknesses of each method, the study determines which techniques are more effective in specific situations. The results provide valuable insights for improving the quality of music streaming services and enhancing user experience. This study aims to help service providers increase user satisfaction by gaining a better understanding of user feedback. Additionally, these analyses are expected to provide valuable data for future improvements in music streaming services. Thus, it will be possible to continuously improve user experiences and enhance service quality.

Balkan Journal of Electrical and Computer EngineeringVol. 13(3)
Ostim Technical University (TR)
Openalex Percentile: Top 70%
Traffic Prediction and Management Techniques
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The Impact of Artificial Intelligence on Sentiment Analysis Detection in Music Reviews — Buğra Kağan Kayhan, Murat Şimşek · Balkan Journal of Electrical and Computer Engineering (2025) | TGRS Research Map | TGRS