X Spotify Cares Clustering Analysis using K-Means and K-Medoids
 (*)Citra Pangestu  (Telkom University, Bandung, Indonesia)
Shaufiah Shaufiah (Telkom University, Bandung, Indonesia)
 Rifki Wijaya (Telkom University, Bandung, Indonesia)
(*) Corresponding Author Submitted: January 5, 2024; Published: January 28, 2024 |
Abstract
The rise of social media platforms, particularly Twitter, has transformed how individuals express opinions and concerns. Companies, like Spotify, leverage platforms such as Twitter for customer support and feedback gathering. This research delves into the world of Spotify Cares tweets using K-Means and K-Medoids clustering methods, aiming to enhance customer support analysis. The study employs the silhouette coefficient and the Davies-Bouldin Index (DBI) to evaluate clustering quality. With an extensive dataset covering more than 3 million Twitter customer service interactions, including 29,479 notes specific to Spotify Cares, this investigation uncovered latent patterns and themes. The versatility of K-Means and K-Medoids, proven effective in a wide range of applications, is highlighted. Therefore, K-means and K-medios were implemented in this research. The results show that K-Means, with 10 clusters (K = 10), with a DBI value of 1.76, shows moderate dispersion, indicating the potential for improvements for better segmentation precision. In contrast, K-Medoids, with 2 clusters (K = 2) and a lower DBI of 1.48, present a clearer and more compact clustering structure. This implies simplified customer categories, which is beneficial for targeted support. In conclusion, although both methods have strengths and weaknesses, K-Medoids with two clusters emerges as a promising method for Spotify Cares, offering cohesive customer groupings for efficient intervention. Future research efforts could focus on refining parameters and exploring the complex relationships between response time, sentiment analysis, and customer satisfaction to achieve a more nuanced analysis.
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References
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1). https://doi.org/10.1007/s11747-019-00695-1.
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2021). A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks. Expert Systems with Applications, 164. https://doi.org/10.1016/j.eswa.2020.114006.
Hodgson, T. (2021). Spotify and the democratisation of music. In Popular Music (Vol. 40, Issue 1). https://doi.org/10.1017/S0261143021000064.
Agnihotri, R. (2020). Social media, customer engagement, and sales organizations: A research agenda. Industrial Marketing Management, 90. https://doi.org/10.1016/j.indmarman.2020.07.017.
Sarin, P., Kar, A. K., & Ilavarasan, V. P. (2021). Exploring engagement among mobile app developers – Insights from mining big data in user generated content. Journal of Advances in Management Research, 18(4). https://doi.org/10.1108/JAMR-06-2020-0128
Tao, D., Yang, P., & Feng, H. (2020). Utilization of text mining as a big data analysis tool for food science and nutrition. In Comprehensive Reviews in Food Science and Food Safety (Vol. 19, Issue 2). https://doi.org/10.1111/1541-4337.12540.
Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2988796.
Ramadhani, S., Azzahra, D., & Z, T. (2022). Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(1). https://doi.org/10.31849/digitalzone.v13i1.9292.
Wahyudi, M., & Pujiastuti, L. (2022). Komparasi K-Means Clustering dan K-Medoids dalam Mengelompokkan Produksi Susu Segar di Indonesia Comparison of K-Means Clustering and K-Medoids in Clustering Fresh Milk Production in Indonesia. Jurnal Bumigora Information Technology (BITe), 4(2).
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2021). A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks. Expert Systems with Applications, 164. https://doi.org/10.1016/j.eswa.2020.114006.
Karami, A., Lundy, M., Webb, F., & Dwivedi, Y. K. (2020). Twitter and Research: A Systematic Literature Review through Text Mining. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2983656.
Shehab, N., Badawy, M., & Arafat, H. (2021). Big Data Analytics and Preprocessing. In Studies in Big Data (Vol. 77). https://doi.org/10.1007/978-3-030-59338-4_2.
Zhang, Y., Safdar, M., Xie, J., Li, J., Sage, M., & Zhao, Y. F. (2023). A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management. In Journal of Intelligent Manufacturing (Vol. 34, Issue 8). https://doi.org/10.1007/s10845-022-02017-9.
Naseem, U., Razzak, I., & Eklund, P. W. (2021). A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimedia Tools and Applications, 80(28–29). https://doi.org/10.1007/s11042-020-10082-6.
Tabassum, A., & Patil, R. R. (2020). A Survey on Text Pre-Processing & Feature Extraction Techniques in Natural Language Processing. International Research Journal of Engineering and Technology, June.
Santos, A., & Paula, H. (2021). Microservice decomposition and evaluation using dependency graph and silhouette coefficient. ACM International Conference Proceeding Series. https://doi.org/10.1145/3483899.3483908.
Kwak, S., Lee, Y., Ko, T., Yang, S., Hwang, I. C., Park, J. B., Yoon, Y. E., Kim, H. L., Kim, H. K., Kim, Y. J., Cho, G. Y., Sohn, D. W., Won, S., & Lee, S. P. (2020). Unsupervised Cluster Analysis of Patients with Aortic Stenosis Reveals Distinct Population with Different Phenotypes and Outcomes. Circulation: Cardiovascular Imaging, 13(5). https://doi.org/10.1161/CIRCIMAGING.119.009707.
Ghosal, A., Nandy, A., Das, A. K., Goswami, S., & Panday, M. (2020). A Short Review on Different Clustering Techniques and Their Applications. Advances in Intelligent Systems and Computing, 937. https://doi.org/10.1007/978-981-13-7403-6_9.
Yan, H., Yang, N., Peng, Y., & Ren, Y. (2020). Data mining in the construction industry: Present status, opportunities, and future trends. In Automation in Construction (Vol. 119). https://doi.org/10.1016/j.autcon.2020.103331.
Gupta, M. K., & Chandra, P. (2020). A comprehensive survey of data mining. International Journal of Information Technology (Singapore), 12(4). https://doi.org/10.1007/s41870-020-00427-7.
Dinata, R. K., Retno, S., & Hasdyna, N. (2021). Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm. Revue d’Intelligence Artificielle, 35(3). https://doi.org/10.18280/ria.350302.
Sharma, K. K., & Seal, A. (2021). Outlier-robust multi-view clustering for uncertain data. Knowledge-Based Systems, 211. https://doi.org/10.1016/j.knosys.2020.106567.
Chai, C. P. (2023). Comparison of text preprocessing methods. Natural Language Engineering, 29(3). https://doi.org/10.1017/S1351324922000213.
Alfarizi, M. I., Syafaah, L., & Lestandy, M. (2022). Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory). JUITA : Jurnal Informatika, 10(2). https://doi.org/10.30595/juita.v10i2.13262.
Prey, R., Esteve Del Valle, M., & Zwerwer, L. (2022). Platform pop: disentangling Spotify’s intermediary role in the music industry. Information Communication and Society, 25(1). https://doi.org/10.1080/1369118X.2020.1761859.
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