Aspect-Level User Sentiment Patterns in Shopee Mobile App Reviews Using TF-IDF and Logistic Regression
DOI:
https://doi.org/10.24076/intechnojournal.2026v8i1.2871Keywords:
Aspect-level analysis, Logistic regression, Mobile app reviews , Sentiment classification , TF-IDFAbstract
Purpose: This study examines recent Shopee mobile application reviews from a service diagnostic perspective by linking sentiment classification with aspect-level interpretation. The aim is to identify not only whether user feedback is positive or negative, but also which service dimensions are associated with satisfaction and dissatisfaction.
Methods: A total of 160,492 Google Play Store reviews of the Shopee Android application were collected from the 2026 review period. After cleaning, rating-based sentiment labeling, and preprocessing, a stratified random sample of 50,000 reviews was used for modeling. TF-IDF unigram and bigram features were applied with a majority class baseline, Naive Bayes, Logistic Regression, and Linear SVM. Performance was evaluated using accuracy, Macro F1-score, confusion matrix, classification report, and 5-fold stratified cross-validation. A keyword-based aspect mapping was then used to interpret sentiment across six service dimensions.
Result: The TF-IDF and Logistic Regression model achieved 0.9194 accuracy, 0.9096 Macro F1-score, and 0.899 cross-validated Macro F1-score. Aspect-level findings show that positive sentiment was concentrated in Promotion and Voucher, Product and Seller, and Customer Service, while negative sentiment was concentrated in Payment, Delivery, and Application Performance. Advertising and delivery-related expressions emerged as key negative signals.
Novelty: This study proposes a service diagnostic task with aspect-level analysis, which includes sentiment classification, term-weight interpretation, and service-aspect mapping, to position Shopee review analysis. The strategy transforms bulk app reviews into actionable data to track platform service quality.
References
[1] D. Pratmanto, R. Rousyati, F. F. Wati, A. E. Widodo, S. Suleman, and R. Wijianto, "App Review Sentiment Analysis Shopee Application in Google Play Store Using Naive Bayes Algorithm," in Journal of Physics: Conference Series, IOP Publishing, 2020, p. 12043.
[2] M. J. Hossain, D. Das Joy, S. Das, and R. Mustafa, "Sentiment analysis on reviews of e-commerce sites using machine learning algorithms," in 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), IEEE, 2022, pp. 522–527.
[3] J. Dabrowski, E. Letier, A. Perini, and A. Susi, "Analysing app reviews for software engineering: a systematic literature review," Empir. Softw. Eng., vol. 27, no. 2, p. 43, 2022.
[4] J. Dabrowski, E. Letier, A. Perini, and A. Susi, "Mining and searching app reviews for requirements engineering: Evaluation and replication studies," Inf. Syst., vol. 114, p. 102181, 2023.
[5] A. A. Qureshi, M. Ahmad, S. Ullah, M. N. Yasir, F. Rustam, and I. Ashraf, "Performance evaluation of machine learning models on large dataset of android applications reviews," Multimed. Tools Appl., vol. 82, no. 24, pp. 37197–37219, 2023.
[6] M. Wankhade, A. C. S. Rao, and C. Kulkarni, "A survey on sentiment analysis methods, applications, and challenges," Artif. Intell. Rev., vol. 55, no. 7, pp. 5731–5780, 2022.
[7] R. Kosasih and A. Alberto, "Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier," Ilk. J. Ilm., vol. 13, no. 2, pp. 101–109, 2021.
[8] N. P. Arthamevia and M. D. Purbolaksono, "Aspect-based sentiment analysis in beauty product reviews using tf-idf and svm algorithm," in 2021 9th International Conference on Information and Communication Technology (ICoICT), IEEE, 2021, pp. 197–201.
[9] Y. Wang, J. Wang, H. Zhang, X. Ming, L. Shi, and Q. Wang, "Where is your app frustrating users?," in Proceedings of the 44th International Conference on Software Engineering, 2022, pp. 2427–2439.
[10] W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, "A survey on aspect-based sentiment analysis: Tasks, methods, and challenges," IEEE Trans. Knowl. Data Eng., vol. 35, no. 11, pp. 11019–11038, 2022.
[11] P. Hajek, L. Hikkerova, and J.-M. Sahut, "Fake review detection in e-Commerce platforms using aspect-based sentiment analysis," J. Bus. Res., vol. 167, p. 114143, 2023.
[12] L. Davoodi, J. Mezei, and M. Heikkila, "Aspect-based sentiment classification of user reviews to understand customer satisfaction of e-commerce platforms," Electron. Commer. Res., vol. 26, no. 2, pp. 1417–1459, 2026.
[13] H. J. Alantari, I. S. Currim, Y. Deng, and S. Singh, "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," Int. J. Res. Mark., vol. 39, no. 1, pp. 1–19, 2022.
[14] A. Daza, N. D. G. Rueda, M. S. A. Sanchez, W. F. R. Espiritu, and M. E. C. Quinones, "Sentiment analysis on e-commerce product reviews using machine learning and deep learning algorithms: A bibliometric analysis, systematic literature review, challenges and future works," Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, p. 100267, 2024.
[15] L. Ashbaugh and Y. Zhang, "A comparative study of sentiment analysis on customer reviews using machine learning and deep learning," Computers, vol. 13, no. 12, p. 340, 2024.
[16] Y. A. Mustofa and I. S. K. Idris, "Ensemble Approach to Sentiment Analysis of Google Play Store App Reviews," Jambura J. Electr. Electron. Eng., vol. 6, no. 2, pp. 181–188, 2024.
[17] P. H. C. Samanmali and R. A. H. M. Rupasingha, "Sentiment analysis on Google Play Store app users reviews based on deep learning approach," Multimed. Tools Appl., vol. 83, no. 36, pp. 84425–84453, 2024.
[18] C. Wang, X. Zhu, and L. Yan, "Sentiment analysis for e-commerce reviews based on deep learning hybrid model," in Proceedings of the 2022 5th International Conference on Signal Processing and Machine Learning, 2022, pp. 38–46.
[19] R. Kusumaningrum, I. Z. Nisa, R. Jayanto, R. P. Nawangsari, and A. Wibowo, "Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews," Heliyon, vol. 9, no. 6, 2023.
[20] R. Sutoyo, S. Achmad, A. Chowanda, E. W. Andangsari, and S. M. Isa, "PRDECT-ID: Indonesian product reviews dataset for emotions classification tasks," Data Br., vol. 44, p. 108554, 2022.
[21] H.-T. Duong and T.-A. Nguyen-Thi, "A review: preprocessing techniques and data augmentation for sentiment analysis," Comput. Soc. Networks, vol. 8, no. 1, p. 1, 2021.
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