Yıl 2025,
Cilt: 13 Sayı: 3, 243 - 252
Murat Şimşek
,
Buğra Kağan Kayhan
Kaynakça
-
[1] Khan, A. W., & Mishra, A. (2023). AI credibility and consumer-AI experiences: a conceptual framework. Journal of Service Theory and Practice.
-
[2] Miragoli, M. (2024). Conformism, Ignorance & Injustice: AI as a Tool of Epistemic Oppression.Episteme.
-
[3] Zheng, J. (2024). Music Sentiment Analysis and its Application in Music Therapy Based on AI Technology. International Journal of Maritime Engineering
-
[4] Chen, Y., & Sun, Y. (2024). The Usage of Artificial Intelligence Technology in Music Education System Under Deep Learning. IEEE Access, 12, 130546-130556
-
[5] Pires, I. M., Zafar, S., Iqbal, K., Sharif, M., Shah, Y. A., Khalil, A., Irfan, M. A., & Rosak-Szyrocka, J. (2024). Attention-aware with stacked embedding for sentiment analysis of student feedback through deep learning techniques. PeerJ Computer Science, 10.
-
[6] Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135. DOI: 10.1561/1500000011
-
[7] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. DOI: 10.2200/S00416ED1V01Y201204HLT016.
-
[8] Jain, N., Kumar, S., & Fernandes, S. L. (2019). Machine Learning Techniques for Sentiment Analysis: A Review. In: Das, H., & Pattnaik, P. (Eds.), Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. DOI: 10.1007/978-981-13-1810-8_19
-
[9] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. DOI: 10.48550/arXiv.1810.04805
-
[10] Zhang, L., Wang, S., & Liu, B. (2020). Deep Learning for Sentiment Analysis: A Survey. IEEE Transactions on Affective Computing, 12(2), 314-332. DOI: 10.1109/TAFFC.2020.2984262
-
[11] Jhanji, R. (2024). Emotion Analysis from Music Using LSTM Models and Mel Spectrograms. Journal of Music Data Science, 8(1), 123-134. DOI: 10.1016/j.jmds.2024.01.001
-
[12] Martin-Gomez, A., Garcia, J., & Lopez, V. (2018). Comparative Study of Multi-Label Classification Algorithms for Emotion Recognition. Journal of Machine Learning Research, 19(45), 1-25.
-
[13] Tilloo, R., Patel, S., & Shah, A. (2021). Sentiment Analysis of Amazon Musical Instrument Reviews Using CNN and NLP Techniques. International Journal of Data Science and Analytics, 12(3), 234-245. DOI: 10.1007/s41060-021-00259-9
-
[14] Website. [Online]. Available: https://www.kaggle.com/datasets/mfaaris/spotify-appreviews-2022
-
[15] Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media.
-
[16] Hovy, E., & Lavid, J. (2010). Towards a ‘science’ of corpus annotation: A new methodological challenge for corpus linguistics. International Journal of Translation, 22(1), 13-36
-
[17] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press
-
[18] Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press
-
[19] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “New Avenues in Opinion Mining and Sentiment Analysis,” Intelligent Systems, IEEE , vol.28, no.2, pp. 15-21, 2013
-
[20] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780
-
[21] Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451-2471.
-
[22] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems
-
[23] Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification?. China National Conference on Chinese Computational Linguistics.
The Impact of Artificial Intelligence on Sentiment Analysis Detection in Music Reviews
Yıl 2025,
Cilt: 13 Sayı: 3, 243 - 252
Murat Şimşek
,
Buğra Kağan Kayhan
Öz
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.
Kaynakça
-
[1] Khan, A. W., & Mishra, A. (2023). AI credibility and consumer-AI experiences: a conceptual framework. Journal of Service Theory and Practice.
-
[2] Miragoli, M. (2024). Conformism, Ignorance & Injustice: AI as a Tool of Epistemic Oppression.Episteme.
-
[3] Zheng, J. (2024). Music Sentiment Analysis and its Application in Music Therapy Based on AI Technology. International Journal of Maritime Engineering
-
[4] Chen, Y., & Sun, Y. (2024). The Usage of Artificial Intelligence Technology in Music Education System Under Deep Learning. IEEE Access, 12, 130546-130556
-
[5] Pires, I. M., Zafar, S., Iqbal, K., Sharif, M., Shah, Y. A., Khalil, A., Irfan, M. A., & Rosak-Szyrocka, J. (2024). Attention-aware with stacked embedding for sentiment analysis of student feedback through deep learning techniques. PeerJ Computer Science, 10.
-
[6] Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135. DOI: 10.1561/1500000011
-
[7] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. DOI: 10.2200/S00416ED1V01Y201204HLT016.
-
[8] Jain, N., Kumar, S., & Fernandes, S. L. (2019). Machine Learning Techniques for Sentiment Analysis: A Review. In: Das, H., & Pattnaik, P. (Eds.), Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. DOI: 10.1007/978-981-13-1810-8_19
-
[9] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. DOI: 10.48550/arXiv.1810.04805
-
[10] Zhang, L., Wang, S., & Liu, B. (2020). Deep Learning for Sentiment Analysis: A Survey. IEEE Transactions on Affective Computing, 12(2), 314-332. DOI: 10.1109/TAFFC.2020.2984262
-
[11] Jhanji, R. (2024). Emotion Analysis from Music Using LSTM Models and Mel Spectrograms. Journal of Music Data Science, 8(1), 123-134. DOI: 10.1016/j.jmds.2024.01.001
-
[12] Martin-Gomez, A., Garcia, J., & Lopez, V. (2018). Comparative Study of Multi-Label Classification Algorithms for Emotion Recognition. Journal of Machine Learning Research, 19(45), 1-25.
-
[13] Tilloo, R., Patel, S., & Shah, A. (2021). Sentiment Analysis of Amazon Musical Instrument Reviews Using CNN and NLP Techniques. International Journal of Data Science and Analytics, 12(3), 234-245. DOI: 10.1007/s41060-021-00259-9
-
[14] Website. [Online]. Available: https://www.kaggle.com/datasets/mfaaris/spotify-appreviews-2022
-
[15] Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media.
-
[16] Hovy, E., & Lavid, J. (2010). Towards a ‘science’ of corpus annotation: A new methodological challenge for corpus linguistics. International Journal of Translation, 22(1), 13-36
-
[17] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press
-
[18] Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press
-
[19] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “New Avenues in Opinion Mining and Sentiment Analysis,” Intelligent Systems, IEEE , vol.28, no.2, pp. 15-21, 2013
-
[20] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780
-
[21] Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451-2471.
-
[22] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems
-
[23] Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification?. China National Conference on Chinese Computational Linguistics.