Analysis of Spotify User Sentiment to Improve Customer Satisfaction Using Opinion Mining and Latent Dirichlet Allocation Based on E-Satisfaction Dimensions

Mutawakkil Samjas; Armin Darmawan

doi:10.24014/ijaidm.v8i3.38480

Analysis of Spotify User Sentiment to Improve Customer Satisfaction Using Opinion Mining and Latent Dirichlet Allocation Based on E-Satisfaction Dimensions

Mutawakkil Samjas, Armin Darmawan

Abstract

This study aims to enhance Spotify customer satisfaction by analyzing user reviews on the Google Play Store using sentiment analysis techniques and identifying relevant topics related to customer satisfaction based on the dimensions of electronic satisfaction. The methods used in this analysis are Support Vector Machine (SVM), Naïve Bayes (NB), and Latent Dirichlet Allocation (LDA). The results show that SVM is the most effective technique for text classification, with accuracies of 87%, 87%, 81%, and 84%, respectively, along with precision, recall, and F1-score of 0.93, 0.93, and 0.84. LDA was utilized to extract various topics within the e-satisfaction dimensions, with serviceability emerging as the top priority for improvement. Identified topics include connectivity and accessibility, performance and user experience, premium services, app quality, content and playlists, app features, and sound/music quality. These findings suggest that improvements in server infrastructure, the implementation of AI-driven chat support, enhanced ad management, and improved song lyrics databases could substantially enhance Spotify's customer satisfaction.

Keywords

E-Satisfaction; Latent Dirichlet Allocation; Naïve Bayes; Sentiment Analysis; Support Vector Machine

Full Text:

PDF

References

“Number of smartphone users in Indonesia from 2019 to 2029 (in millions). Statista. 2024,” Statista.com. [Online]. Available: https://www.statista.com/forecasts/266729/smartphone-users-in-indonesia?srsltid=AfmBOopd3MuSzEK62T70cYdpG3LbwP8vawD6fiLG-_xhEYLYk_6wAsjM

N. Adisty, “Mengulik perkembangan penggunaan smartphone di Indonesia,” goodstats.id. [Online]. Available: https://goodstats.id/article/mengulik-perkembangan-penggunaan-smartphone-di-indonesia-sT2LA

C. M. Annur, “Durasi penggunaan ponsel di Indonesia cenderung meningkat semenjak pandemi,” databoks.katadata.co.id. [Online]. Available: https://databoks.katadata.co.id/teknologi-telekomunikasi/statistik/147c4723c1d145f/durasi-penggunaan-ponsel-di-indonesia-cenderung-meningkat-semenjak-pandemi

A. S. Rahayu and A. Fauzi, “Komparasi Algoritma Naïve Bayes Dan Support Vector Machine ( SVM ) Pada Analisis Sentimen Spotify,” vol. 4, pp. 349–354, 2022, doi: 10.30865/json.v4i2.5398.

N. Naurah, “Bukan Spotify, Youtube Music Juarai Platform Musik yang Paling Sering Digunakan Masyarakat 2023,” goodstats.id. [Online]. Available: https://goodstats.id/article/bukan-spotify-youtube-music-juarai-platform-musik-yang-paling-sering-digunakan-masyarakat-2023-o2Uyq

D. Noviani, R. Pratiwi, S. Silvianadewi, M. Benny Alexandri, and M. Aulia Hakim, “Pengaruh Streaming Musik Terhadap Industri Musik di Indonesia,” J. Bisnis Strateg., vol. 29, no. 1, pp. 14–25, 2020, doi: 10.14710/jbs.29.1.14-25.

D. P. Sepnandito and Suharyati, “Pengaruh Fitur Spotify Social Dan PemasaranPersonalisasi Terhadap Loyalitas PelangganSpotify Dimediasi Dengan Kepuasan Pelanggan,” J. Young Entrep., vol. 3, pp. 49–65, 2024, [Online]. Available: https://ejournal.upnvj.ac.id/index.php/jye

H. A. Pradana, P. Pujiati, and N. Nina, “Hubungan Kualitas Pelayanan, Promosi dan Kepuasan Pelanggan terhadap Loyalitas Pelanggan Griya Sehat Kementerian Kesehatan Tahun 2021,” J. Public Heal. Educ., vol. 3, no. 2, pp. 42–50, 2024, doi: 10.53801/jphe.v3i2.178.

H. S. Le et al., “Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learning,” Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, p. 100295, 2024, doi: 10.1016/j.jjimei.2024.100295.

K. S. Chong and N. Shah, “Comparison of Naive Bayes and SVM Classification in Grid-Search Hyperparameter Tuned and Non-Hyperparameter Tuned Healthcare Stock Market Sentiment Analysis,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 12, pp. 90–94, 2022, doi: 10.14569/IJACSA.2022.0131213.

M. A. Rahman and M. H. Seddiqui, “Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion Analysis,” pp. 2–7, 2019, [Online]. Available: http://arxiv.org/abs/1907.07826

M. Guia, R. R. Silva, and J. Bernardino, “Comparison of Naive Bayes, support vector machine, decision trees and random forest on sentiment analysis,” IC3K 2019 - Proc. 11th Int. Jt. Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag., vol. 1, no. Ic3k, pp. 525–531, 2019, doi: 10.5220/0008364105250531.

A. Juwaini et al., “The role of customer e-trust, customer e-service quality and customer e-satisfaction on customer e-loyalty,” Int. J. Data Netw. Sci., vol. 6, no. 2, pp. 477–486, 2022, doi: 10.5267/j.ijdns.2021.12.006.

S. Asnaniyah, “Pengaruh E-Service Quality, E-Trust Dan E-Satisfaction Terhadap E-Loyalty Konsumen Muslim,” J. Compr. Islam. Stud., vol. 1, no. 2, pp. 275–302, 2022, doi: 10.56436/jocis.v1i2.142.

A. Farhan AlShammari, “Implementation of Keyword Extraction using Term Frequency-Inverse Document Frequency (TF-IDF) in Python,” Int. J. Comput. Appl., vol. 185, no. 35, pp. 975–8887, 2023.

N. Yusliani, S. A. Q. Aruda, M. D. Marieska, D. M. Saputra, and A. Abdiansah, “The effect of Chi-Square Feature Selection on Question Classification using Multinomial Naïve Bayes,” Sinkron, vol. 7, no. 4, pp. 2430–2436, 2022, doi: 10.33395/sinkron.v7i4.11788.

I. Darmawan, O. N. Pratiwi, F. R. Industri, and U. Telkom, “Analisis Sentimen Ulasan Produk Toko Online Rubylicious Untuk,” e-Proceeding Eng., vol. 7, no. 2, pp. 7026–7034, 2020.

E. Fitri, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine,” J. Transform., vol. 18, no. 1, pp. 71–80, 2020, doi: 10.26623/transformatika.v18i1.2317.

H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, pp. 18–26, 2021, doi: 10.34148/teknika.v10i1.311.

S. Karmila and V. I. Ardianti, “Metode Latent Dirichlet Allocation Untuk Menentukan Topik Teks Suatu Berita,” J. Inform. dan Komputasi Media Bahasan, Anal. dan Apl., vol. 16, no. 01, pp. 36–44, 2022, doi: 10.56956/jiki.v16i01.100.

T. Titiana and D. H. Bangkalang, “Analisis Dan Penerapan Topic Modeling Pada Judul Tugas Akhir Mahasiswa Menggunakan Metode Latent Dirichlet Allocation (Lda),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 8, no. 4, pp. 1275–1287, 2023, doi: 10.29100/jipi.v8i4.4254.

J. O. Leandro and M. I. Fianty, “Evaluation of Sentiment Analysis Methods for Social Media Applications: A Comparison of Support Vector Machines and Naïve Bayes,” Int. J. Informatics Vis., vol. 9, no. 2, pp. 796–807, 2025, doi: 10.62527/joiv.9.2.2905.

F. S. Nahm, “Receiver operating characteristic curve : overview and practical use for clinicians,” 2022.

S. Syed and M. Spruit, “Exploring Symmetrical and Asymmetrical Dirichlet Priors for Latent Dirichlet Allocation,” Int. J. Semant. Comput., vol. 12, no. 3, pp. 399–423, 2018, doi: 10.1142/S1793351X18400184.

M. Röder, A. Both, and A. Hinneburg, “Exploring the space of topic coherence measures,” WSDM 2015 - Proc. 8th ACM Int. Conf. Web Search Data Min., pp. 399–408, 2015, doi: 10.1145/2684822.2685324.

G. Satria, P. Ramadhan, and S. Nugroho, “Optimasi Data Preprocessing dan Hyperparameter Tuning pada Klasifikasi Penyakit Daun Apel menggunakan DenseNet169,” vol. 6, no. 3, pp. 1352–1362, 2024, doi: 10.47065/bits.v6i3.6134.

“Ideal Dataset Splitting Ratios In Machine Learning Algorithms :,” pp. 496–504, 2021.

H. Bichri, A. Chergui, and M. Hain, “Investigating the Impact of Train / Test Split Ratio on the Performance of Pre-Trained Models with Custom Datasets,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 2, pp. 331–339, 2024, doi: 10.14569/IJACSA.2024.0150235.

Y. Nuri and E. Senyurek, “Research Abstracts Similarity Implementation By Using TF-IDF Algorithm,” vol. 27, no. 1, pp. 4–10, 2025, doi: 10.9790/0661-2701040410.

X. Cheng, “A Comprehensive Study of Feature Selection Techniques in Machine Learning Models,” Insights Comput. Signals Syst., vol. 1, no. 1, pp. 65–78, 2024, doi: 10.70088/xpf2b276.

S. F. Sandita, I. T. Utami, and M. Y. Rochayani, “Sentiment Classification of Livin by Mandiri App Reviews with Support Vector Machine Based on Modified Particle Swarm Optimization,” Int. J. Math. Comput. Res., vol. 13, no. 05, pp. 5260–5266, 2025, doi: 10.47191/ijmcr/v13i5.18.

N. Fajriyah, N. T. Lapatta, D. W. Nugraha, and R. Laila, “Implementasi Svm Dan Smote Pada Analisis Sentimen Media Sosial X Terhadap Pelantikan Agus Harimurti Yudhoyono,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 2, pp. 1359–1370, 2025, doi: 10.29100/jipi.v10i2.6246.

R. D. Fitriani, H. Yasin, and T. Tarno, “Penanganan Klasifikasi Kelas Data Tidak Seimbang Dengan Random Oversampling Pada Naive Bayes (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal),” J. Gaussian, vol. 10, no. 1, pp. 11–20, 2021, doi: 10.14710/j.gauss.v10i1.30243.

DOI: http://dx.doi.org/10.24014/ijaidm.v8i3.38480

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats