TY - JOUR T1 - The Impact of Artificial Intelligence on Sentiment Analysis Detection in Music Reviews AU - Şimşek, Murat AU - Kayhan, Buğra Kağan PY - 2025 DA - October Y2 - 2025 DO - 10.17694/bajece.1492287 JF - Balkan Journal of Electrical and Computer Engineering PB - MUSA YILMAZ WT - DergiPark SN - 2147-284X SP - 243 EP - 252 VL - 13 IS - 3 LA - en AB - 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. KW - Sentiment Analysis KW - Machine Learning KW - Deep Learning KW - Large Language Model KW - Natural Language Processing KW - LSTM CR - [1] Khan, A. W., & Mishra, A. (2023). AI credibility and consumer-AI experiences: a conceptual framework. Journal of Service Theory and Practice. CR - [2] Miragoli, M. (2024). Conformism, Ignorance & Injustice: AI as a Tool of Epistemic Oppression.Episteme. 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