Araştırma Makalesi

DOI :10.26650/acin.1604272 IUP :10.26650/acin.1604272 Tam Metin (PDF)

Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches

Spotify, with over 320 million monthly active users reported in 2020, offers a unique platform for data science and machine learning applications. This study leverages Spotify’s extensive music library of over 50 million songs to analyze the emotional tone of user-created playlists using machine learning algorithms. By employing advanced classification methods, including Random Forest, Decision Tree, and Support Vector Machines (SVM), the research compares their effectiveness in sentiment classification tasks. The Random Forest model achieved the highest test accuracy of 87%, closely followed by the Decision Tree model at 86%. These results highlight the potential of sentiment-informed data to enhance music recommendation systems by tailoring suggestions to users’ emotional preferences. This work not only contributes to the evolving domain of sentiment-aware recommendation models but also demonstrates the technical challenges and practical implications of applying machine learning in music streaming platforms. The study’s findings underscore the value of integrating emotional intelligence into recommendation algorithms to improve user engagement and satisfaction in digital music services.

Anahtar Kelimeler: Sentiment Analysis, Spotify API, Playlist, Machine Learning, Music Recommendation Systems

Referanslar

AbeL, F., Gao, Q., Houben, G. J. & Tao, K. (2011). Analyzing user modeling on Twitter for personalized news recommendations. User ModeLing, Adaption, and PersonaLization, 1-12. Springer. google scholar
BirdaL, R. G. (2024). The Influence of Air PoLLution Concentrations on SoLar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction. CMC-Computers Materials and Continua, 78(3), 4015-4028. google scholar
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. google scholar
Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD InternationaL Conference on KnowLedge Discovery and Data Mining, 785-794. google scholar
DeLbouys, R., Hennequin, R., PiccoLi, F., Royo-LeteLier, J. & MoussaLLam, M. (2018). Music mood detection based on audio and lyrics with deep neural net. Proceedings of the 19th InternationaL Society for Music Information RetrievaL Conference (ISMIR), 688-695. google scholar
Ferraro, S., Bogdanov, D., Yoon, S., Kim, Y., & Serra, X. (2018). Cross-cultural analysis of user behavior in music streaming services. Proceedings of the 19th InternationaL Society for Music Information RetrievaL Conference (ISMIR), 1-6. google scholar
Hu, M. & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the 10th ACM SIGKDD InternationaL Conference on KnowLedge Discovery and Data Mining, 168-177. google scholar
Hutto, C. J., & GiLbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th InternationaL Conference on WebLogs and SociaL Media (ICWSM), 216-225. google scholar
Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011). Target-dependent Twitter sentiment classification. Proceedings of the 49th Annual Meeting of the Association for ComputationaL Linguistics: Human Language Technologies, 151-160. google scholar
Li, Y., Wang, X., Chen, J., & Xu, Z. (2023). Sentiment anaLysis on music recommendation: A convoLutionaL neuraL network approach. IEEE Transactions on Multimedia, 25(3), 689-700. google scholar
Liu, H., & Singh, P. (2004). ConceptNet: A practicaL commonsense reasoning tooLkit. BT Technology Journal, 22(4), 211-226. google scholar
MumcuoğLu, K. Y. (2007). Biotherapy laboratory protocol department of parasitology. IsraeL: Hebrew University-Hadassah MedicaL SchooL JerusaLem. google scholar
Pang, B. & Lee, L. (2008). Opinion mining and sentiment anaLysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. google scholar
Pappas, N. & Popescu-BeLis, A. (2016). Sentiment analysis of user comments for one-class collaborative filtering over text-based recom-mendations. Proceedings of the 39th InternationaL ACM SIGIR Conference on Research and DeveLopment in Information RetrievaL, 773-776. google scholar
Sancar, Y. (2024). Enhanced CLassification of Skin Lesions Using Fine-Tuned MobiLeNet and DenseNet121 ModeLs with EnsembLe Learning. Erzincan University Journal of Science and Technology, 17(3), 870-883. google scholar
SchedL, M. (2016). The LFM-1b dataset for music recommendation and user modeling. Proceedings of the 2016 ACM on InternationaL Conference on MuLtimedia RetrievaL, 103-110. google scholar
SchedL, M., Knees, P., McFee, B., & Bogdanov, D. (2021). Music recommendation: State of the art and chaLLenges. IEEE Signal Processing Magazine, 38(3), 29-44. google scholar
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th AnnuaL Meeting of the Association for ComputationaL Linguistics, 417-424. google scholar
Zhang, J., Li, H., Zhao, R., & Huang, Y. (2022). Comparative study of LSTM and GRU for music sentiment anaLysis. IEEE Transactions on Neural Networks and Learning Systems, 33(5), 2345-2356. google scholar

Atıflar

Biçimlendirilmiş bir atıfı kopyalayıp yapıştırın veya seçtiğiniz biçimde dışa aktarmak için seçeneklerden birini kullanın

DIŞA AKTAR

APA

İsenkul, M.E. (2019). Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches. Acta Infologica, 0(0), -. https://doi.org/10.26650/acin.1604272

AMA

İsenkul M E. Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches. Acta Infologica. 2019;0(0):-. https://doi.org/10.26650/acin.1604272

ABNT

İsenkul, M.E. Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches. Acta Infologica, [Publisher Location], v. 0, n. 0, p. -, 2019.

Chicago: Author-Date Style

İsenkul, M. Erdem,. 2019. “Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches.” Acta Infologica 0, no. 0: -. https://doi.org/10.26650/acin.1604272

Chicago: Humanities Style

İsenkul, M. Erdem,. “Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches.” Acta Infologica 0, no. 0 (Mar. 2025): -. https://doi.org/10.26650/acin.1604272

Harvard: Australian Style

İsenkul, ME 2019, 'Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches', Acta Infologica, vol. 0, no. 0, pp. -, viewed 10 Mar. 2025, https://doi.org/10.26650/acin.1604272

Harvard: Author-Date Style

İsenkul, M.E. (2019) ‘Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches’, Acta Infologica, 0(0), pp. -. https://doi.org/10.26650/acin.1604272 (10 Mar. 2025).

MLA

İsenkul, M. Erdem,. “Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches.” Acta Infologica, vol. 0, no. 0, 2019, pp. -. [Database Container], https://doi.org/10.26650/acin.1604272

Vancouver

İsenkul ME. Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches. Acta Infologica [Internet]. 10 Mar. 2025 [cited 10 Mar. 2025];0(0):-. Available from: https://doi.org/10.26650/acin.1604272 doi: 10.26650/acin.1604272

ISNAD

İsenkul, M.Erdem. “Optimizing the Sentiment Recognition in Spotify Playlists Through Ensemble-Based Approaches”. Acta Infologica 0/0 (Mar. 2025): -. https://doi.org/10.26650/acin.1604272

ZAMAN ÇİZELGESİ

Gönderim	19.12.2024
Kabul	24.02.2025
Çevrimiçi Yayınlanma	26.02.2025

LİSANS

Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.

Acta Infologica