An Analytical Study on Emotional Responses to the Reinterpretation of Popular Music Works by Artificial Intelligence

Year 2025, Volume: 14 Issue: 4, 63 - 88

Nazmi Ekin Vural , Selçuk Çetin

https://doi.org/10.15869/itobiad.1685888

Abstract

In this research, the emotional effects of popular music works reinterpreted through artificial intelligence technologies on listeners, as well as their cultural and aesthetic transformations, were examined. Through purposive sampling, seven music videos representing various genres were selected from the YouTube platform. The comments made by listeners on the videos were obtained using Python-based software through the YouTube API tool. The emotional tones in the comments were classified as positive, negative, and neutral, and emotion maps were produced. Additionally, the comments were subjected to thematic analysis using qualitative data techniques. Evaluations were conducted based on seven themes. According to the findings of the research, a large portion of the audience responded positively to AI-generated music; however, a significant segment displayed indecisive or critical attitudes in their technological and aesthetic evaluations. According to the results of the thematic analysis, curiosity was the most emphasized phenomenon by listeners. This shows that the audience considers music reproduced by artificial intelligence not only as an element of entertainment but also as a technological innovation and a learning opportunity. Emotional intensity and attachment were other themes highlighted by listeners. Accordingly, it is revealed that the comments regarding AI-generated music establish a strong connection with the audience, particularly through elements of popular culture and nostalgic references. Some listeners, although finding the music technically successful, emphasized that artificial productions create a deficiency in emotional transmission. It was particularly identified that the concepts of mechanicalness and alienation became prominent. This situation also shows that artificial intelligence cannot fully reflect the human touch in music. AI-supported music production will continue to spread depending on technological developments. Being aware of this reality, technological innovations in the musical field should be approached holistically with the common perspective of all stakeholders, without neglecting issues such as ethics, aesthetics, and copyright.

Keywords

Technological Innovation , Artificial Intelligence , YouTube , Emotional Responses , Popular Music , Sentiment Analysis , Thematic Analysis.

Popüler Müzik Eserlerinin Yapay Zekâ Tarafından Yeniden Yorumlanması: Duygusal Tepkiler Üzerine Bir Analiz

Abstract

Bu çalışmada, yapay zekâ teknolojileriyle yeniden yorumlanan popüler müzik eserlerinin dinleyiciler üzerindeki duygusal etkileri ile kültürel ve estetik dönüşümleri incelenmiştir. Amaçlı örneklem yoluyla YouTube platformunda çeşitli müzik türlerini temsil eden yedi müzik ele alınmıştır. Dinleyicilerin videolara getirdiği yorumlar, YouTube API aracı kullanılarak Python tabanlı yazılım ile elde edilmiştir. Yorumlardaki duygusal tonlar pozitif, negatif ve nötr olarak sınıflandırılmış ve duygu haritaları ortaya çıkarılmıştır. Ayrıca, yorumlar nitel veri tekniği ile tematik analize tabi tutulmuştur. Yedi tema üzerinden değerlendirmeler yapılmıştır. Araştırmanın bulgularına göre, dinleyici kitlesinin büyük bir bölümünün yapay zekâ ile üretilen müzikleri olumlu karşıladığını, ancak anlamlı bir kısmının da teknolojik ve estetik değerlendirmelerde kararsız veya eleştirel tutum içerisinde olduğunu göstermiştir. Yorumların tematik analiz sonuçlarına göre dinleyiciler en çok merak olgusunu ön plana çıkarmıştır. Bu da dinleyici kitlesinin yapay zekâ ile yeniden üretilen müzikleri yalnızca eğlence unsuru olarak değil, teknolojik bir yenilik ve öğrenme fırsatı olarak değerlendirdiğini göstermiştir. Duygusal yoğunluk ve bağlanma dinleyiciler tarafından ön plana çıkan diğer temadır. Buna göre, yapay zekâ ile üretilen müziğe dair yorumların özellikle popüler kültür unsurları ve nostaljik referanslar üzerinden dinleyiciyle güçlü bir ilişki kurduğunu ortaya koymaktadır. Bazı dinleyiciler, müzikleri teknik açıdan başarılı bulsa da yapay üretimlerin duygusal aktarım açısından eksiklik yarattığını vurgulamıştır. Özellikle mekaniklik ve yabancılaşma kavramlarının ön plana çıktığı tespit edilmiştir. Bu durum, yapay zekânın müzikte insani dokunuşu tam anlamıyla yansıtamadığını da göstermektedir. Yapay zekâ destekli müzik üretimi teknolojik gelişmelere bağlı olarak yaygınlaşmaya devam edecektir. Bu gerçekliğin farkında olarak; etik, estetik ve telif hakkı gibi konuları da göz ardı etmeden, müzikal alanda teknolojik yeniliklere tüm paydaşların ortak yaklaşımı ile bütünsel bakmak gerektiği açıktır.

Keywords

Teknolojik Yenilik , Yapay Zekâ , YouTube , Duygusal Tepkiler , Popüler Müzik , Duygu Analizi , Tematik Analiz.

References

• Agwan, M., Nemade, M., Sinha, U., & Roy, S. (2023). The fusion of AI and music generation: A comprehensive review.2023 6th International Conference on Advances in Science and Technology (ICAST). IEEE.
• Ahmed, M., Rozario, U., Kabir, M. M., Aung, Z., Shin, J., & Mridha, M. F. (2024). Musical genre classification using advanced audio analysis and deep learning techniques. IEEE Open Journal of Computer Science, 5, 457–467.
• Ajay, A., & Rajan, R. (2023). Music genre classification using attention-based CNN-feature fusion paradigm. 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS). IEEE.
• Akkol, M. L. (2018). Müzik sosyolojisinde T. W. Adorno’nun yeri. Alternatif Politika, 10(1), 111–130. Retrieved from http://www.alternatifpolitika.com
• Allamy, S., & Koerich, A. L. (2021). 1D CNN architectures for music genre classification. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE.
• Avdeeff, M. (2019). Artificial intelligence & popular music: SKYGGE, Flow Machines, and the audio uncanny valley.Arts, 8(4), 130.
• Aydar, D. (2014). Popüler kültür ve müzik üzerine. Uluslararası Sosyal Araştırmalar Dergisi, 7(33), 800–807. Retrieved from http://www.sosyalarastirmalar.com
• Barton, G. (2018). Music Learning and Teaching in Culturally and Socially Diverse Contexts, Palgrave Macmillan, USA.
• Berkowitz, A. E. (2024). Artificial intelligence and musicking: A philosophical inquiry. Music Perception, 41(5), 393–412.
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
• Britzolakis, A., Kondylakis, H., & Papadakis, N. (2020). A review on lexicon-based and machine learning political sentiment analysis using tweets. International Journal of Semantic Computing, 14(4), 507–541.
• Chen, J., Ma, X., Li, S., Ma, S., Zhang, Z., & Ma, X. (2024). A hybrid parallel computing architecture based on CNN and Transformer for music genre classification. Electronics, 13(3313).
• Dallas, L., & Morreale, F. (2024). Effects of added vocals and human production to AI-composed music on listener’s appreciation. Proceedings of the International Conference on Artificial Intelligence and Music. University of Auckland.
• Fox, M., Vaidyanathan, G., & Breese, J. L. (2024). The impact of artificial intelligence on musicians. Issues in Information Systems, 25(3), 267–276.
• Galuszka, P. (2024). The influence of generative AI on popular music: Fan productions and the reimagination of iconic voices. Media, Culture & Society, 47(3), 603-612. https://doi.org/10.1177/01634437241282382. (Original work published 2025)
• Gao, X., Chen, D., Gou, Z., Ma, L., Liu, R., Zhao, D., & Ham, J. (2024). AI-driven music generation and emotion conversion. Affective and Pleasurable Design, 123, 82–93.
• Gündoğdu, B., & Okcu, S. (2024). Effects of artificial intelligence applications on music and the music industry.Konservatoryum – Conservatorium, 11(2), 545–558.
• Han, D., Kong, Y., Han, J., & Wang, G. (2022). A survey of music emotion recognition. Frontiers of Computer Science, 16(6), 166335.
• Hilchenko, C., & Taubman-Bassirian, T. (2023). Artificial intelligence and ethics. Journal of Education, Technology and Computer Science, 4(34), 119–134.
• Hutson, J., & Ratican, J. (2023). Life, death, and AI: Exploring digital necromancy in popular culture—Ethical considerations, technological limitations, and the pet cemetery conundrum. Metaverse, 4(1), 1–12.
• Hutto, C. J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. In Proceedings of the Eighth International Conference on Weblogs and Social Media (ICWSM-14) (pp. 216–225).
• Katyal, Y., Dhasmana, G., Singh, S. V., & Saxena, A. (2024). Exploring the evolution of music and artificial intelligence.2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE). IEEE.
• Kumar, S. (2024). Music and technology. Sangeet Galaxy, 13(1), 200–206. Retrieved from http://www.sangeetgalaxy.co.in
• Kurniawan, D. K., Alexander, G. R., & Sidharta, S. (2023). Deep learning for music: A systematic literature review. 2023 International Conference on Information Management and Technology (ICIMTech). IEEE.
• Kossanova, A.S., Yermanov, Z.R., Bekenova, A.S., Julmukhamedova, A.A., Takezhanova, R.P. & Zhussupova, S.S. (2016). Music as the Representative of the World Picture, the Phenomenon of Culture. International Journal of Environmental and Science Education, 11(12), 5171-5181.
• Lu, G. (2023). Deep learning-based music generation. Proceedings of the 2023 International Conference on Software Engineering and Machine Learning.
• Moura, F. T., & Maw, C. (2021). Artificial intelligence became Beethoven: How do listeners and music professionals perceive artificially composed music? Journal of Consumer Marketing, 38(2), 137–146.
• Park, J., Choi, Y., & Lee, K. M. (2024). Research trends in virtual reality music concert technology: A systematic literature review. IEEE Transactions on Visualization and Computer Graphics, 30(5), 2195–2204.
• Rajeswari, S., & Dhanalakshmi, R. (2023). Differentiate music genre from an audio file using CNN. Proceedings of IEEE International Conference on System, Computation, Automation, and Networking (ICSCAN 2023), 1–8.
• Rodríguez Reséndiz, H., & Rodríguez Reséndiz, J. (2024). Digital resurrection: Challenging the boundary between life and death with artificial intelligence. Philosophies, 9(3), 71.
• Ser, A. (2023). Algoritmaların senfonisi: Müzikte yapay zekanın geçmişi, bugünü ve geleceğinin değerlendirilmesi. MAS Uygulamalı Bilimler Dergisi, 8(2), 320–328.
• Smart, A. (2024). Music and technology. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Elon University.
• Tahiroğlu, K. (2021). Ever-shifting roles in building, composing and performing with digital musical instruments.Journal of New Music Research, 50(2), 155–164.
• Turchet, L., Hamilton, R., & Çamcı, A. (2021). Music in extended realities. IEEE Access, 9, 15810–15823.
• Warnars, H. L. H. S., & Rusli, W. (2022). A literature review of music in computer science. International Journal of Computing and Digital Systems, 12(1), 1503–1516.
• Widdess, R. (2012). Music, meaning, and culture. Empirical Musicology Review, 7(1–2), 88–94. Retrieved from https://emusicology.org
• Xu, L. (2023). A study on the fair use principles of artificial intelligence-generated music. Proceedings of the 2nd International Conference on Interdisciplinary Humanities and Communication Studies.
• Yang, T., & Nazir, S. (2022). A comprehensive overview of AI-enabled music classification and its influence in games.Soft Computing, 26, 7679–7693
• Zulić, H. (2019). How AI can change, improve, and influence music composition, performance, and education: Three case studies. INSAM Journal of Contemporary Music, Art and Technology, 2(1), 100–114.