SENTIMENT ANALYSIS BASED ON THE PERFORMANCE ASPECTS OF THE TIKTOK APP BY TIME PERIOD AND APP VERSION USING SUPPORT VECTOR MACHINES (SVM)

Irna Dwi Astuti, Departemen Pendidikan Matematika, Universitas Negeri Yogyakarta, Indonesia
Bambang Sumarno Hadi Marwoto, Departemen Pendidikan Matematika, Universitas Negeri Yogyakarta, Indonesia

 10.21831/jktm.v12i3.29295

Abstract


This study aims to analyze aspect-based sentiment regarding the performance of the TikTok app based on user reviews on Google Play, as well as to identify changes in user perceptions over time and across app versions following app updates. The performance aspects analyzed include speed, stability, and responsiveness. This study employs a quantitative method with a descriptive approach. Data was obtained through web scraping of TikTok user reviews on Google Play during the period of January 1–December 31, 2025. The data was processed through the stages of text preprocessing, sentiment labeling using the INSET lexicon, aspect labeling using a keyword-based approach, feature representation using TF-IDF, and classification using the Support Vector Machine (SVM) method. The results of the study indicate that SVM is capable of effectively classifying sentiment across all aspects of the app’s performance. Speed and stability were the most frequently discussed aspects and were dominated by negative sentiment. Additionally, the analysis revealed that app updates influenced changes in user perceptions of TikTok’s performance throughout 2025.

Keywords:  aspect-based sentiment analysis, TikTok, SVM.


Full Text:

PDF

References


Aggarwal, C. C., & Xhai, C. (2012). A survery of text clustring algorithms. Mining Text Data, 8, 77–128. http://link.springer.com/10.1007/978-1-4614-3223-4

Ainunnisa, I. R., & Sulastri, S. (2023). Analisis Sentimen Aplikasi Tiktok dengan Metode Support Vector Machine (SVM), Logistic Regression dan Naïve Bayes. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 6(3), 423–430. https://doi.org/10.32493/jtsi.v6i3.31076

Husain, N. P., Sukirman, S., & SAJIAH, S. (2024). Analisis Sentimen Ulasan Pengguna Tiktok pada Google Play Store Berbasis TF-IDF dan Support Vector Machine. Journal of System and Computer Engineering (JSCE), 5(1), 91–102. https://doi.org/10.61628/jsce.v5i1.1105

Kaplan, A. M., & Haenlein, M. (2010). Users of the world , unite ! The challenges and opportunities of Social Media. https://doi.org/10.1016/j.bushor.2009.09.003

Koto, F., & Rahmaningtyas, G. Y. (2017). Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017, 2018-Janua, 391–394. https://doi.org/10.1109/IALP.2017.8300625

Lehman, M. M. (1980). Programs, Life Cycles, and Laws of Software Evolution. Proceedings of the IEEE, 68(9), 1060–1076. https://doi.org/10.1109/PROC.1980.11805

Liu, B. (2012). Sentiment Analysis and Opinion Mining. (May).

Martin, W., Sarro, F., Jia, Y., Zhang, Y., & Harman, M. (2017). A survey of app store analysis for software engineering. IEEE Transactions on Software Engineering, 43(9), 817–847. https://doi.org/10.1109/TSE.2016.2630689

Pagano, D., & Maalej, W. (2013). User Feedback in the AppStore : An Empirical Study.

Raemaekers, S., van Deursen, A., & Visser, J. (2017). Semantic versioning and impact of breaking changes in the Maven repository. Journal of Systems and Software, 129, 140–158. https://doi.org/10.1016/j.jss.2016.04.008




DOI: https://doi.org/10.21831/jktm.v12i3.29295

Refbacks

  • There are currently no refbacks.


 

Online ISSN (e-ISSN): 3031-1152

Creative Commons LicenseJurnal Kajian dan Terapan Matematika by https://journal.student.uny.ac.id/index.php/jktm/index is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
 
View My Stats